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README.md
277
README.md
@@ -1,80 +1,106 @@
|
||||
# Allegro Trains Agent
|
||||
## Deep Learning DevOps For Everyone - Now supporting all platforms (Linux, macOS, and Windows)
|
||||
<div align="center">
|
||||
|
||||
"All the Deep-Learning DevOps your research needs, and then some... Because ain't nobody got time for that"
|
||||
<img src="https://github.com/allegroai/clearml-agent/blob/master/docs/clearml_agent_logo.png?raw=true" width="250px">
|
||||
|
||||
**ClearML Agent - ML-Ops made easy
|
||||
ML-Ops scheduler & orchestration solution supporting Linux, macOS and Windows**
|
||||
|
||||
[](https://img.shields.io/github/license/allegroai/trains-agent.svg)
|
||||
[](https://img.shields.io/pypi/pyversions/trains-agent.svg)
|
||||
[](https://img.shields.io/pypi/v/trains-agent.svg)
|
||||
[](https://pypi.python.org/pypi/trains-agent/)
|
||||
[](https://img.shields.io/pypi/pyversions/clearml-agent.svg)
|
||||
[](https://img.shields.io/pypi/v/clearml-agent.svg)
|
||||
|
||||
### Help improve Trains by filling our 2-min [user survey](https://allegro.ai/lp/trains-user-survey/)
|
||||
</div>
|
||||
|
||||
**Trains Agent is an AI experiment cluster solution.**
|
||||
---
|
||||
|
||||
It is a zero configuration fire-and-forget execution agent, which combined with trains-server provides a full AI cluster solution.
|
||||
### ClearML-Agent
|
||||
#### *Formerly known as Trains Agent*
|
||||
|
||||
**Full AutoML in 5 steps**
|
||||
1. Install the [Trains Server](https://github.com/allegroai/trains-agent) (or use our [open server](https://demoapp.trains.allegro.ai))
|
||||
2. `pip install trains-agent` ([install](#installing-the-trains-agent) the Trains Agent on any GPU machine: on-premises / cloud / ...)
|
||||
3. Add [Trains](https://github.com/allegroai/trains) to your code with just 2 lines & run it once (on your machine / laptop)
|
||||
4. Change the [parameters](#using-the-trains-agent) in the UI & schedule for [execution](#using-the-trains-agent) (or automate with an [AutoML pipeline](#automl-and-orchestration-pipelines-))
|
||||
|
||||
* Run jobs (experiments) on any local or cloud based resource
|
||||
* Implement optimized resource utilization policies
|
||||
* Deploy execution environments with either virtualenv or fully docker containerized with zero effort
|
||||
* Launch-and-Forget service containers
|
||||
* [Cloud autoscaling](https://allegro.ai/clearml/docs/examples/services/aws_autoscaler/aws_autoscaler/)
|
||||
* [Customizable cleanup](https://allegro.ai/clearml/docs/examples/services/cleanup/cleanup_service/)
|
||||
* Advanced [pipeline building and execution](https://allegro.ai/clearml/docs/examples/frameworks/pytorch/notebooks/table/tabular_training_pipeline/)
|
||||
|
||||
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)
|
||||
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
|
||||
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"
|
||||
|
||||
**Using the Trains Agent, you can now set up a dynamic cluster with \*epsilon DevOps**
|
||||
**Try ClearML now** [Self Hosted](https://github.com/allegroai/trains-server) or [Free tier Hosting](https://app.community.clear.ml)
|
||||
<a href="https://app.community.clear.ml"><img src="https://raw.githubusercontent.com/allegroai/trains-agent/9f1e86c1ca45c984ee13edc9353c7b10c55d7257/docs/screenshots.gif" width="100%"></a>
|
||||
|
||||
*epsilon - Because we are scientists :triangular_ruler: and nothing is really zero work
|
||||
|
||||
(Experience Trains live at [https://demoapp.trains.allegro.ai](https://demoapp.trains.allegro.ai))
|
||||
<a href="https://demoapp.trains.allegro.ai"><img src="https://raw.githubusercontent.com/allegroai/trains-agent/9f1e86c1ca45c984ee13edc9353c7b10c55d7257/docs/screenshots.gif" width="100%"></a>
|
||||
|
||||
## Simple, Flexible Experiment Orchestration
|
||||
**The Trains Agent was built to address the DL/ML R&D DevOps needs:**
|
||||
### Simple, Flexible Experiment Orchestration
|
||||
**The ClearML Agent was built to address the DL/ML R&D DevOps needs:**
|
||||
|
||||
* Easily add & remove machines from the cluster
|
||||
* Reuse machines without the need for any dedicated containers or images
|
||||
* **Combine GPU resources across any cloud and on-prem**
|
||||
* **No need for yaml/json/template configuration of any kind**
|
||||
* **No need for yaml / json / template configuration of any kind**
|
||||
* **User friendly UI**
|
||||
* Manageable resource allocation that can be used by researchers and engineers
|
||||
* Flexible and controllable scheduler with priority support
|
||||
* Automatic instance spinning in the cloud **(coming soon)**
|
||||
* Automatic instance spinning in the cloud
|
||||
|
||||
**Using the ClearML Agent, you can now set up a dynamic cluster with \*epsilon DevOps**
|
||||
|
||||
*epsilon - Because we are :triangular_ruler: and nothing is really zero work
|
||||
|
||||
|
||||
## But ... K8S?
|
||||
We think Kubernetes is awesome.
|
||||
Combined with KubeFlow it is a robust solution for production-grade DevOps.
|
||||
We've observed, however, that it can be a bit of an overkill as an R&D DL/ML solution.
|
||||
If you are considering K8S for your research, also consider that you will soon be managing **hundreds** of containers...
|
||||
### Kubernetes Integration (Optional)
|
||||
We think Kubernetes is awesome, but it should be a choice.
|
||||
We designed `clearml-agent` so you can run bare-metal or inside a pod with any mix that fits your environment.
|
||||
#### Benefits of integrating existing K8s with ClearML-Agent
|
||||
- ClearML-Agent adds the missing scheduling capabilities to K8s
|
||||
- Allowing for more flexible automation from code
|
||||
- A programmatic interface for easier learning curve (and debugging)
|
||||
- Seamless integration with ML/DL experiment manager
|
||||
- Web UI for customization, scheduling & prioritization of jobs
|
||||
|
||||
In our experience, handling and building the environments, having to package every experiment in a docker, managing those hundreds (or more) containers and building pipelines on top of it all, is very complicated (also, it’s usually out of scope for the research team, and overwhelming even for the DevOps team).
|
||||
**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
|
||||
- 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
|
||||
- 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
|
||||
- downside: No real scheduling (k8s scheduler), no docker image verification (post-mortem only)
|
||||
|
||||
We feel there has to be a better way, that can be just as powerful for R&D and at the same time allow integration with K8S **when the need arises**.
|
||||
(If you already have a K8S cluster for AI, detailed instructions on how to integrate Trains into your K8S cluster are [here](https://github.com/allegroai/trains-server-k8s/tree/master/trains-server-chart) with included [helm chart](https://github.com/allegroai/trains-server-helm))
|
||||
|
||||
|
||||
## Using the Trains Agent
|
||||
### Using the ClearML Agent
|
||||
**Full scale HPC with a click of a button**
|
||||
|
||||
The Trains Agent is a job scheduler that listens on job queue(s), pulls jobs, sets the job environments, executes the job and monitors its progress.
|
||||
The ClearML Agent is a job scheduler that listens on job queue(s), pulls jobs, sets the job environments, executes the job and monitors its progress.
|
||||
|
||||
Any 'Draft' experiment can be scheduled for execution by a Trains agent.
|
||||
Any 'Draft' experiment can be scheduled for execution by a ClearML agent.
|
||||
|
||||
A previously run experiment can be put into 'Draft' state by either of two methods:
|
||||
* Using the **'Reset'** action from the experiment right-click context menu in the
|
||||
Trains UI - This will clear any results and artifacts the previous run had created.
|
||||
ClearML UI - This will clear any results and artifacts the previous run had created.
|
||||
* Using the **'Clone'** action from the experiment right-click context menu in the
|
||||
Trains UI - This will create a new 'Draft' experiment with the same configuration as the original experiment.
|
||||
ClearML UI - This will create a new 'Draft' experiment with the same configuration as the original experiment.
|
||||
|
||||
An experiment is scheduled for execution using the **'Enqueue'** action from the experiment
|
||||
right-click context menu in the Trains UI and selecting the execution queue.
|
||||
right-click context menu in the ClearML UI and selecting the execution queue.
|
||||
|
||||
See [creating an experiment and enqueuing it for execution](#from-scratch).
|
||||
|
||||
Once an experiment is enqueued, it will be picked up and executed by a Trains agent monitoring this queue.
|
||||
Once an experiment is enqueued, it will be picked up and executed by a ClearML agent monitoring this queue.
|
||||
|
||||
The Trains UI Workers & Queues page provides ongoing execution information:
|
||||
The ClearML UI Workers & Queues page provides ongoing execution information:
|
||||
- Workers Tab: Monitor you cluster
|
||||
- Review available resources
|
||||
- Monitor machines statistics (CPU / GPU / Disk / Network)
|
||||
@@ -83,162 +109,129 @@ The Trains UI Workers & Queues page provides ongoing execution information:
|
||||
- Cancel or abort job execution
|
||||
- Move jobs between execution queues
|
||||
|
||||
### What The Trains Agent Actually Does
|
||||
The Trains Agent executes experiments using the following process:
|
||||
#### What The ClearML Agent Actually Does
|
||||
The ClearML Agent executes experiments using the following process:
|
||||
- Create a new virtual environment (or launch the selected docker image)
|
||||
- Clone the code into the virtual-environment (or inside the docker)
|
||||
- Install python packages based on the package requirements listed for the experiment
|
||||
- Special note for PyTorch: The Trains Agent will automatically select the
|
||||
- Special note for PyTorch: The ClearML Agent will automatically select the
|
||||
torch packages based on the CUDA_VERSION environment variable of the machine
|
||||
- Execute the code, while monitoring the process
|
||||
- Log all stdout/stderr in the Trains UI, including the cloning and installation process, for easy debugging
|
||||
- Monitor the execution and allow you to manually abort the job using the Trains UI (or, in the unfortunate case of a code crash, catch the error and signal the experiment has failed)
|
||||
- Log all stdout/stderr in the ClearML UI, including the cloning and installation process, for easy debugging
|
||||
- Monitor the execution and allow you to manually abort the job using the ClearML UI (or, in the unfortunate case of a code crash, catch the error and signal the experiment has failed)
|
||||
|
||||
### System Design & Flow
|
||||
```text
|
||||
+-----------------+
|
||||
| GPU Machine |
|
||||
Development Machine | |
|
||||
+------------------------+ | +-------------+ |
|
||||
| Data Scientist's | +--------------+ | |Trains Agent | |
|
||||
| DL/ML Code | | WEB UI | | | | |
|
||||
| | | | | | +---------+ | |
|
||||
| | | | | | | DL/ML | | |
|
||||
| | +--------------+ | | | Code | | |
|
||||
| | User Clones Exp #1 / . . . . . . . / | | | | | |
|
||||
| +-------------------+ | into Exp #2 / . . . . . . . / | | +---------+ | |
|
||||
| | Trains | | +---------------/-_____________-/ | | | |
|
||||
| +---------+---------+ | | | | ^ | |
|
||||
+-----------|------------+ | | +------|------+ |
|
||||
| | +--------|--------+
|
||||
Auto-Magically | |
|
||||
Creates Exp #1 | The Trains Agent
|
||||
\ User Change Hyper-Parameters Pulls Exp #2, setup the
|
||||
| | environment & clone code.
|
||||
| | Start execution with the
|
||||
+------------|------------+ | +--------------------+ new set of Hyper-Parameters.
|
||||
| +---------v---------+ | | | Trains Server | |
|
||||
| | Experiment #1 | | | | | |
|
||||
| +-------------------+ | | | Execution Queue | |
|
||||
| || | | | | |
|
||||
| +-------------------+<----------+ | | |
|
||||
| | | | | | |
|
||||
| | Experiment #2 | | | | |
|
||||
| +-------------------<------------\ | | |
|
||||
| | ------------->---------------+ | |
|
||||
| | User Send Exp #2 | |Execute Exp #2 +--------------------+
|
||||
| | For Execution | +---------------+ |
|
||||
| Trains Server | | |
|
||||
+-------------------------+ +--------------------+
|
||||
```
|
||||
#### System Design & Flow
|
||||
|
||||
### Installing the Trains Agent
|
||||
<img src="https://allegro.ai/clearml/docs/img/ClearML_Architecture.png" width="100%" alt="clearml-architecture">
|
||||
|
||||
|
||||
#### Installing the ClearML Agent
|
||||
|
||||
```bash
|
||||
pip install trains-agent
|
||||
pip install clearml-agent
|
||||
```
|
||||
|
||||
### Trains Agent Usage Examples
|
||||
#### ClearML Agent Usage Examples
|
||||
|
||||
Full Interface and capabilities are available with
|
||||
```bash
|
||||
trains-agent --help
|
||||
trains-agent daemon --help
|
||||
clearml-agent --help
|
||||
clearml-agent daemon --help
|
||||
```
|
||||
|
||||
### Configuring the Trains Agent
|
||||
#### Configuring the ClearML Agent
|
||||
|
||||
```bash
|
||||
trains-agent init
|
||||
clearml-agent init
|
||||
```
|
||||
|
||||
Note: The Trains Agent uses a cache folder to cache pip packages, apt packages and cloned repositories. The default Trains Agent cache folder is `~/.trains`
|
||||
Note: The ClearML Agent uses a cache folder to cache pip packages, apt packages and cloned repositories. The default ClearML Agent cache folder is `~/.clearml`
|
||||
|
||||
See full details in your configuration file at `~/trains.conf`
|
||||
See full details in your configuration file at `~/clearml.conf`
|
||||
|
||||
Note: The **Trains agent** extends the **Trains** configuration file `~/trains.conf`
|
||||
They are designed to share the same configuration file, see example [here](docs/trains.conf)
|
||||
Note: The **ClearML agent** extends the **ClearML** configuration file `~/clearml.conf`
|
||||
They are designed to share the same configuration file, see example [here](docs/clearml.conf)
|
||||
|
||||
### Running the Trains Agent
|
||||
#### Running the ClearML Agent
|
||||
|
||||
For debug and experimentation, start the Trains agent in `foreground` mode, where all the output is printed to screen
|
||||
For debug and experimentation, start the ClearML agent in `foreground` mode, where all the output is printed to screen
|
||||
```bash
|
||||
trains-agent daemon --queue default --foreground
|
||||
clearml-agent daemon --queue default --foreground
|
||||
```
|
||||
|
||||
For actual service mode, all the stdout will be stored automatically into a temporary file (no need to pipe)
|
||||
Notice: with `--detached` flag, the *trains-agent* will be running in the background
|
||||
Notice: with `--detached` flag, the *clearml-agent* will be running in the background
|
||||
```bash
|
||||
trains-agent daemon --detached --queue default
|
||||
clearml-agent daemon --detached --queue default
|
||||
```
|
||||
|
||||
GPU allocation is controlled via the standard OS environment `NVIDIA_VISIBLE_DEVICES` or `--gpus` flag (or disabled with `--cpu-only`).
|
||||
|
||||
If no flag is set, and `NVIDIA_VISIBLE_DEVICES` variable doesn't exist, all GPU's will be allocated for the `trains-agent` <br>
|
||||
If `--cpu-only` flag is set, or `NVIDIA_VISIBLE_DEVICES` is an empty string (""), no gpu will be allocated for the `trains-agent`
|
||||
If no flag is set, and `NVIDIA_VISIBLE_DEVICES` variable doesn't exist, all GPU's will be allocated for the `clearml-agent` <br>
|
||||
If `--cpu-only` flag is set, or `NVIDIA_VISIBLE_DEVICES` is an empty string (""), no gpu will be allocated for the `clearml-agent`
|
||||
|
||||
Example: spin two agents, one per gpu on the same machine:
|
||||
Notice: with `--detached` flag, the *trains-agent* will be running in the background
|
||||
Notice: with `--detached` flag, the *clearml-agent* will be running in the background
|
||||
```bash
|
||||
trains-agent daemon --detached --gpus 0 --queue default
|
||||
trains-agent daemon --detached --gpus 1 --queue default
|
||||
clearml-agent daemon --detached --gpus 0 --queue default
|
||||
clearml-agent daemon --detached --gpus 1 --queue default
|
||||
```
|
||||
|
||||
Example: spin two agents, pulling from dedicated `dual_gpu` queue, two gpu's per agent
|
||||
```bash
|
||||
trains-agent daemon --detached --gpus 0,1 --queue dual_gpu
|
||||
trains-agent daemon --detached --gpus 2,3 --queue dual_gpu
|
||||
clearml-agent daemon --detached --gpus 0,1 --queue dual_gpu
|
||||
clearml-agent daemon --detached --gpus 2,3 --queue dual_gpu
|
||||
```
|
||||
|
||||
#### Starting the Trains Agent in docker mode
|
||||
##### Starting the ClearML Agent in docker mode
|
||||
|
||||
For debug and experimentation, start the Trains agent in `foreground` mode, where all the output is printed to screen
|
||||
For debug and experimentation, start the ClearML agent in `foreground` mode, where all the output is printed to screen
|
||||
```bash
|
||||
trains-agent daemon --queue default --docker --foreground
|
||||
clearml-agent daemon --queue default --docker --foreground
|
||||
```
|
||||
|
||||
For actual service mode, all the stdout will be stored automatically into a file (no need to pipe)
|
||||
Notice: with `--detached` flag, the *trains-agent* will be running in the background
|
||||
Notice: with `--detached` flag, the *clearml-agent* will be running in the background
|
||||
```bash
|
||||
trains-agent daemon --detached --queue default --docker
|
||||
clearml-agent daemon --detached --queue default --docker
|
||||
```
|
||||
|
||||
Example: spin two agents, one per gpu on the same machine, with default nvidia/cuda docker:
|
||||
```bash
|
||||
trains-agent daemon --detached --gpus 0 --queue default --docker nvidia/cuda
|
||||
trains-agent daemon --detached --gpus 1 --queue default --docker nvidia/cuda
|
||||
clearml-agent daemon --detached --gpus 0 --queue default --docker nvidia/cuda
|
||||
clearml-agent daemon --detached --gpus 1 --queue default --docker nvidia/cuda
|
||||
```
|
||||
|
||||
Example: spin two agents, pulling from dedicated `dual_gpu` queue, two gpu's per agent, with default nvidia/cuda docker:
|
||||
```bash
|
||||
trains-agent daemon --detached --gpus 0,1 --queue dual_gpu --docker nvidia/cuda
|
||||
trains-agent daemon --detached --gpus 2,3 --queue dual_gpu --docker nvidia/cuda
|
||||
clearml-agent daemon --detached --gpus 0,1 --queue dual_gpu --docker nvidia/cuda
|
||||
clearml-agent daemon --detached --gpus 2,3 --queue dual_gpu --docker nvidia/cuda
|
||||
```
|
||||
|
||||
#### Starting the Trains Agent - Priority Queues
|
||||
##### Starting the ClearML Agent - Priority Queues
|
||||
|
||||
Priority Queues are also supported, example use case:
|
||||
|
||||
High priority queue: `important_jobs` Low priority queue: `default`
|
||||
```bash
|
||||
trains-agent daemon --queue important_jobs default
|
||||
clearml-agent daemon --queue important_jobs default
|
||||
```
|
||||
The **Trains Agent** will first try to pull jobs from the `important_jobs` queue, only then it will fetch a job from the `default` queue.
|
||||
The **ClearML Agent** will first try to pull jobs from the `important_jobs` queue, only then it will fetch a job from the `default` queue.
|
||||
|
||||
Adding queues, managing job order within a queue and moving jobs between queues, is available using the Web UI, see example on our [open server](https://demoapp.trains.allegro.ai/workers-and-queues/queues)
|
||||
Adding queues, managing job order within a queue and moving jobs between queues, is available using the Web UI, see example on our [free server](https://app.community.clear.ml/workers-and-queues/queues)
|
||||
|
||||
#### Stopping the Trains Agent
|
||||
##### Stopping the ClearML Agent
|
||||
|
||||
To stop a **Trains Agent** running in the background, run the same command line used to start the agent with `--stop` appended.
|
||||
To stop a **ClearML Agent** running in the background, run the same command line used to start the agent with `--stop` appended.
|
||||
For example, to stop the first of the above shown same machine, single gpu agents:
|
||||
```bash
|
||||
trains-agent daemon --detached --gpus 0 --queue default --docker nvidia/cuda --stop
|
||||
clearml-agent daemon --detached --gpus 0 --queue default --docker nvidia/cuda --stop
|
||||
```
|
||||
|
||||
## How do I create an experiment on the Trains Server? <a name="from-scratch"></a>
|
||||
* Integrate [Trains](https://github.com/allegroai/trains) with your code
|
||||
### How do I create an experiment on the ClearML Server? <a name="from-scratch"></a>
|
||||
* Integrate [ClearML](https://github.com/allegroai/trains) with your code
|
||||
* Execute the code on your machine (Manually / PyCharm / Jupyter Notebook)
|
||||
* As your code is running, **Trains** creates an experiment logging all the necessary execution information:
|
||||
* 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)
|
||||
- Git diff (we’re not saying you never commit and push, but still...)
|
||||
- Python packages used by your code (including specific versions used)
|
||||
@@ -247,7 +240,7 @@ trains-agent daemon --detached --gpus 0 --queue default --docker nvidia/cuda --s
|
||||
|
||||
You now have a 'template' of your experiment with everything required for automated execution
|
||||
|
||||
* In the Trains UI, Right click on the experiment and select 'clone'. A copy of your experiment will be created.
|
||||
* In the ClearML UI, Right click on the experiment and select 'clone'. A copy of your experiment will be created.
|
||||
* You now have a new draft experiment cloned from your original experiment, feel free to edit it
|
||||
- Change the Hyper-Parameters
|
||||
- Switch to the latest code base of the repository
|
||||
@@ -256,31 +249,31 @@ trains-agent daemon --detached --gpus 0 --queue default --docker nvidia/cuda --s
|
||||
- Or simply change nothing to run the same experiment again...
|
||||
* Schedule the newly created experiment for execution: Right-click the experiment and select 'enqueue'
|
||||
|
||||
## Trains-Agent Services Mode <a name="services"></a>
|
||||
### ClearML-Agent Services Mode <a name="services"></a>
|
||||
|
||||
Trains-Agent Services is a special mode of Trains-Agent that provides the ability to launch long-lasting jobs
|
||||
that previously had to be executed on local / dedicated machines. It allows a single agent to
|
||||
launch multiple dockers (Tasks) for different use cases. To name a few use cases, auto-scaler service (spinning instances
|
||||
ClearML-Agent Services is a special mode of ClearML-Agent that provides the ability to launch long-lasting jobs
|
||||
that previously had to be executed on local / dedicated machines. It allows a single agent to
|
||||
launch multiple dockers (Tasks) for different use cases. To name a few use cases, auto-scaler service (spinning instances
|
||||
when the need arises and the budget allows), Controllers (Implementing pipelines and more sophisticated DevOps logic),
|
||||
Optimizer (such as Hyper-parameter Optimization or sweeping), and Application (such as interactive Bokeh apps for
|
||||
Optimizer (such as Hyper-parameter Optimization or sweeping), and Application (such as interactive Bokeh apps for
|
||||
increased data transparency)
|
||||
|
||||
Trains-Agent Services mode will spin **any** task enqueued into the specified queue.
|
||||
Every task launched by Trains-Agent Services will be registered as a new node in the system,
|
||||
providing tracking and transparency capabilities.
|
||||
Currently trains-agent in services-mode supports cpu only configuration. Trains-agent services mode can be launched alongside GPU agents.
|
||||
ClearML-Agent Services mode will spin **any** task enqueued into the specified queue.
|
||||
Every task launched by ClearML-Agent Services will be registered as a new node in the system,
|
||||
providing tracking and transparency capabilities.
|
||||
Currently clearml-agent in services-mode supports cpu only configuration. ClearML-agent services mode can be launched alongside GPU agents.
|
||||
|
||||
```bash
|
||||
trains-agent daemon --services-mode --detached --queue services --create-queue --docker ubuntu:18.04 --cpu-only
|
||||
clearml-agent daemon --services-mode --detached --queue services --create-queue --docker ubuntu:18.04 --cpu-only
|
||||
```
|
||||
|
||||
**Note**: It is the user's responsibility to make sure the proper tasks are pushed into the specified queue.
|
||||
**Note**: It is the user's responsibility to make sure the proper tasks are pushed into the specified queue.
|
||||
|
||||
|
||||
## AutoML and Orchestration Pipelines <a name="automl-pipes"></a>
|
||||
The Trains Agent can also be used to implement AutoML orchestration and Experiment Pipelines in conjunction with the Trains package.
|
||||
### AutoML and Orchestration Pipelines <a name="automl-pipes"></a>
|
||||
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 Trains [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/trains/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)
|
||||
@@ -294,6 +287,6 @@ Experiment Pipeline examples
|
||||
- [Second step experiment](https://github.com/allegroai/trains/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
|
||||
### License
|
||||
|
||||
Apache License, Version 2.0 (see the [LICENSE](https://www.apache.org/licenses/LICENSE-2.0.html) for more information)
|
||||
|
||||
@@ -4,13 +4,13 @@ import argparse
|
||||
import sys
|
||||
import warnings
|
||||
|
||||
from trains_agent.backend_api.session.datamodel import UnusedKwargsWarning
|
||||
from clearml_agent.backend_api.session.datamodel import UnusedKwargsWarning
|
||||
|
||||
import trains_agent
|
||||
from trains_agent.config import get_config
|
||||
from trains_agent.definitions import FileBuffering, CONFIG_FILE
|
||||
from trains_agent.helper.base import reverse_home_folder_expansion, chain_map, named_temporary_file
|
||||
from trains_agent.helper.process import ExitStatus
|
||||
import clearml_agent
|
||||
from clearml_agent.config import get_config
|
||||
from clearml_agent.definitions import FileBuffering, CONFIG_FILE
|
||||
from clearml_agent.helper.base import reverse_home_folder_expansion, chain_map, named_temporary_file
|
||||
from clearml_agent.helper.process import ExitStatus
|
||||
from . import interface, session, definitions, commands
|
||||
from .errors import ConfigFileNotFound, Sigterm, APIError
|
||||
from .helper.trace import PackageTrace
|
||||
@@ -47,7 +47,7 @@ def run_command(parser, args, command_name):
|
||||
except ConfigFileNotFound:
|
||||
message = 'Cannot find configuration file in "{}".\n' \
|
||||
'To create a configuration file, run:\n' \
|
||||
'$ trains_agent init'.format(reverse_home_folder_expansion(CONFIG_FILE))
|
||||
'$ clearml_agent init'.format(reverse_home_folder_expansion(CONFIG_FILE))
|
||||
command_class.exit(message)
|
||||
except APIError as api_error:
|
||||
if not debug:
|
||||
@@ -1,12 +1,12 @@
|
||||
{
|
||||
# unique name of this worker, if None, created based on hostname:process_id
|
||||
# Override with os environment: TRAINS_WORKER_ID
|
||||
# worker_id: "trains-agent-machine1:gpu0"
|
||||
# Override with os environment: CLEARML_WORKER_ID
|
||||
# worker_id: "clearml-agent-machine1:gpu0"
|
||||
worker_id: ""
|
||||
|
||||
# worker name, replaces the hostname when creating a unique name for this worker
|
||||
# Override with os environment: TRAINS_WORKER_NAME
|
||||
# worker_name: "trains-agent-machine1"
|
||||
# Override with os environment: CLEARML_WORKER_NAME
|
||||
# worker_name: "clearml-agent-machine1"
|
||||
worker_name: ""
|
||||
|
||||
# Set GIT user/pass credentials (if user/pass are set, GIT protocol will be set to https)
|
||||
@@ -22,7 +22,7 @@
|
||||
|
||||
# Set the python version to use when creating the virtual environment and launching the experiment
|
||||
# Example values: "/usr/bin/python3" or "/usr/local/bin/python3.6"
|
||||
# The default is the python executing the trains_agent
|
||||
# The default is the python executing the clearml_agent
|
||||
python_binary: ""
|
||||
|
||||
# select python package manager:
|
||||
@@ -42,7 +42,7 @@
|
||||
force_upgrade: false,
|
||||
|
||||
# additional artifact repositories to use when installing python packages
|
||||
# extra_index_url: ["https://allegroai.jfrog.io/trainsai/api/pypi/public/simple"]
|
||||
# extra_index_url: ["https://allegroai.jfrog.io/clearmlai/api/pypi/public/simple"]
|
||||
|
||||
# additional conda channels to use when installing with conda package manager
|
||||
conda_channels: ["defaults", "conda-forge", "pytorch", ]
|
||||
@@ -73,12 +73,12 @@
|
||||
},
|
||||
|
||||
# target folder for virtual environments builds, created when executing experiment
|
||||
venvs_dir = ~/.trains/venvs-builds
|
||||
venvs_dir = ~/.clearml/venvs-builds
|
||||
|
||||
# cached git clone folder
|
||||
vcs_cache: {
|
||||
enabled: true,
|
||||
path: ~/.trains/vcs-cache
|
||||
path: ~/.clearml/vcs-cache
|
||||
},
|
||||
|
||||
# use venv-update in order to accelerate python virtual environment building
|
||||
@@ -90,7 +90,7 @@
|
||||
# cached folder for specific python package download (used for pytorch package caching)
|
||||
pip_download_cache {
|
||||
enabled: true,
|
||||
path: ~/.trains/pip-download-cache
|
||||
path: ~/.clearml/pip-download-cache
|
||||
},
|
||||
|
||||
translate_ssh: true,
|
||||
@@ -98,9 +98,9 @@
|
||||
reload_config: false,
|
||||
|
||||
# pip cache folder mapped into docker, used for python package caching
|
||||
docker_pip_cache = ~/.trains/pip-cache
|
||||
docker_pip_cache = ~/.clearml/pip-cache
|
||||
# apt cache folder mapped into docker, used for ubuntu package caching
|
||||
docker_apt_cache = ~/.trains/apt-cache
|
||||
docker_apt_cache = ~/.clearml/apt-cache
|
||||
|
||||
# optional arguments to pass to docker image
|
||||
# these are local for this agent and will not be updated in the experiment's docker_cmd section
|
||||
@@ -1,10 +1,10 @@
|
||||
{
|
||||
# TRAINS - default SDK configuration
|
||||
# ClearML - default SDK configuration
|
||||
|
||||
storage {
|
||||
cache {
|
||||
# Defaults to system temp folder / cache
|
||||
default_base_dir: "~/.trains/cache"
|
||||
default_base_dir: "~/.clearml/cache"
|
||||
size {
|
||||
# max_used_bytes = -1
|
||||
min_free_bytes = 10GB
|
||||
@@ -98,7 +98,7 @@
|
||||
google.storage {
|
||||
# # Default project and credentials file
|
||||
# # Will be used when no bucket configuration is found
|
||||
# project: "trains"
|
||||
# project: "clearml"
|
||||
# credentials_json: "/path/to/credentials.json"
|
||||
|
||||
# # Specific credentials per bucket and sub directory
|
||||
@@ -106,7 +106,7 @@
|
||||
# {
|
||||
# bucket: "my-bucket"
|
||||
# subdir: "path/in/bucket" # Not required
|
||||
# project: "trains"
|
||||
# project: "clearml"
|
||||
# credentials_json: "/path/to/credentials.json"
|
||||
# },
|
||||
# ]
|
||||
@@ -114,7 +114,7 @@
|
||||
azure.storage {
|
||||
# containers: [
|
||||
# {
|
||||
# account_name: "trains"
|
||||
# account_name: "clearml"
|
||||
# account_key: "secret"
|
||||
# # container_name:
|
||||
# }
|
||||
@@ -155,8 +155,8 @@
|
||||
# do not analyze the entire repository.
|
||||
force_analyze_entire_repo: false
|
||||
|
||||
# If set to true, *trains* update message will not be printed to the console
|
||||
# this value can be overwritten with os environment variable TRAINS_SUPPRESS_UPDATE_MESSAGE=1
|
||||
# If set to true, *clearml* update message will not be printed to the console
|
||||
# this value can be overwritten with os environment variable CLEARML_SUPPRESS_UPDATE_MESSAGE=1
|
||||
suppress_update_message: false
|
||||
|
||||
# If this flag is true (default is false), instead of analyzing the code with Pigar, analyze with `pip freeze`
|
||||
@@ -106,15 +106,15 @@ class StrictSession(Session):
|
||||
init()
|
||||
return
|
||||
|
||||
original = os.environ.get(LOCAL_CONFIG_FILE_OVERRIDE_VAR, None)
|
||||
original = LOCAL_CONFIG_FILE_OVERRIDE_VAR.get() or None
|
||||
try:
|
||||
os.environ[LOCAL_CONFIG_FILE_OVERRIDE_VAR] = str(config_file)
|
||||
LOCAL_CONFIG_FILE_OVERRIDE_VAR.set(str(config_file))
|
||||
init()
|
||||
finally:
|
||||
if original is None:
|
||||
os.environ.pop(LOCAL_CONFIG_FILE_OVERRIDE_VAR, None)
|
||||
LOCAL_CONFIG_FILE_OVERRIDE_VAR.pop()
|
||||
else:
|
||||
os.environ[LOCAL_CONFIG_FILE_OVERRIDE_VAR] = original
|
||||
LOCAL_CONFIG_FILE_OVERRIDE_VAR.set(original)
|
||||
|
||||
def send(self, request, *args, **kwargs):
|
||||
result = super(StrictSession, self).send(request, *args, **kwargs)
|
||||
@@ -222,7 +222,7 @@ class TableResponse(Response):
|
||||
return "" if result is None else result
|
||||
|
||||
fields = fields or self.fields
|
||||
from trains_agent.helper.base import create_table
|
||||
from clearml_agent.helper.base import create_table
|
||||
return create_table(
|
||||
(dict((attr, getter(item, attr)) for attr in fields) for item in self),
|
||||
titles=fields, columns=fields, headers=True,
|
||||
11
clearml_agent/backend_api/session/defs.py
Normal file
11
clearml_agent/backend_api/session/defs.py
Normal file
@@ -0,0 +1,11 @@
|
||||
from ...backend_config.environment import EnvEntry
|
||||
|
||||
|
||||
ENV_HOST = EnvEntry("CLEARML_API_HOST", "TRAINS_API_HOST")
|
||||
ENV_WEB_HOST = EnvEntry("CLEARML_WEB_HOST", "TRAINS_WEB_HOST")
|
||||
ENV_FILES_HOST = EnvEntry("CLEARML_FILES_HOST", "TRAINS_FILES_HOST")
|
||||
ENV_ACCESS_KEY = EnvEntry("CLEARML_API_ACCESS_KEY", "TRAINS_API_ACCESS_KEY")
|
||||
ENV_SECRET_KEY = EnvEntry("CLEARML_API_SECRET_KEY", "TRAINS_API_SECRET_KEY")
|
||||
ENV_VERBOSE = EnvEntry("CLEARML_API_VERBOSE", "TRAINS_API_VERBOSE", type=bool, default=False)
|
||||
ENV_HOST_VERIFY_CERT = EnvEntry("CLEARML_API_HOST_VERIFY_CERT", "TRAINS_API_HOST_VERIFY_CERT", type=bool, default=True)
|
||||
ENV_CONDA_ENV_PACKAGE = EnvEntry("CLEARML_CONDA_ENV_PACKAGE", "TRAINS_CONDA_ENV_PACKAGE")
|
||||
@@ -29,12 +29,12 @@ class MaxRequestSizeError(Exception):
|
||||
|
||||
|
||||
class Session(TokenManager):
|
||||
""" TRAINS API Session class. """
|
||||
""" ClearML API Session class. """
|
||||
|
||||
_AUTHORIZATION_HEADER = "Authorization"
|
||||
_WORKER_HEADER = "X-Trains-Worker"
|
||||
_ASYNC_HEADER = "X-Trains-Async"
|
||||
_CLIENT_HEADER = "X-Trains-Agent"
|
||||
_WORKER_HEADER = ("X-ClearML-Worker", "X-Trains-Worker", )
|
||||
_ASYNC_HEADER = ("X-ClearML-Async", "X-Trains-Async", )
|
||||
_CLIENT_HEADER = ("X-ClearML-Agent", "X-Trains-Agent", )
|
||||
|
||||
_async_status_code = 202
|
||||
_session_requests = 0
|
||||
@@ -45,9 +45,9 @@ class Session(TokenManager):
|
||||
_write_session_timeout = (30.0, 30.)
|
||||
|
||||
api_version = '2.1'
|
||||
default_host = "https://demoapi.trains.allegro.ai"
|
||||
default_web = "https://demoapp.trains.allegro.ai"
|
||||
default_files = "https://demofiles.trains.allegro.ai"
|
||||
default_host = "https://demoapi.demo.clear.ml"
|
||||
default_web = "https://demoapp.demo.clear.ml"
|
||||
default_files = "https://demofiles.demo.clear.ml"
|
||||
default_key = "EGRTCO8JMSIGI6S39GTP43NFWXDQOW"
|
||||
default_secret = "x!XTov_G-#vspE*Y(h$Anm&DIc5Ou-F)jsl$PdOyj5wG1&E!Z8"
|
||||
|
||||
@@ -192,8 +192,10 @@ class Session(TokenManager):
|
||||
"""
|
||||
host = self.host
|
||||
headers = headers.copy() if headers else {}
|
||||
headers[self._WORKER_HEADER] = self.worker
|
||||
headers[self._CLIENT_HEADER] = self.client
|
||||
for h in self._WORKER_HEADER:
|
||||
headers[h] = self.worker
|
||||
for h in self._CLIENT_HEADER:
|
||||
headers[h] = self.client
|
||||
|
||||
token_refreshed_on_error = False
|
||||
url = (
|
||||
@@ -268,7 +270,8 @@ class Session(TokenManager):
|
||||
headers.copy() if headers else {}
|
||||
)
|
||||
if async_enable:
|
||||
headers[self._ASYNC_HEADER] = "1"
|
||||
for h in self._ASYNC_HEADER:
|
||||
headers[h] = "1"
|
||||
return self._send_request(
|
||||
service=service,
|
||||
action=action,
|
||||
@@ -464,7 +467,7 @@ class Session(TokenManager):
|
||||
if parsed.port == 8008:
|
||||
return host.replace(':8008', ':8080', 1)
|
||||
|
||||
raise ValueError('Could not detect TRAINS web application server')
|
||||
raise ValueError('Could not detect ClearML web application server')
|
||||
|
||||
@classmethod
|
||||
def get_files_server_host(cls, config=None):
|
||||
@@ -548,13 +551,13 @@ class Session(TokenManager):
|
||||
# check if this is a misconfigured api server (getting 200 without the data section)
|
||||
if res and res.status_code == 200:
|
||||
raise ValueError('It seems *api_server* is misconfigured. '
|
||||
'Is this the TRAINS API server {} ?'.format(self.get_api_server_host()))
|
||||
'Is this the ClearML API server {} ?'.format(self.get_api_server_host()))
|
||||
else:
|
||||
raise LoginError("Response data mismatch: No 'token' in 'data' value from res, receive : {}, "
|
||||
"exception: {}".format(res, ex))
|
||||
except requests.ConnectionError as ex:
|
||||
raise ValueError('Connection Error: it seems *api_server* is misconfigured. '
|
||||
'Is this the TRAINS API server {} ?'.format('/'.join(ex.request.url.split('/')[:3])))
|
||||
'Is this the ClearML API server {} ?'.format('/'.join(ex.request.url.split('/')[:3])))
|
||||
except Exception as ex:
|
||||
raise LoginError('Unrecognized Authentication Error: {} {}'.format(type(ex), ex))
|
||||
|
||||
@@ -107,7 +107,7 @@ def get_http_session_with_retry(
|
||||
if not session.verify and __disable_certificate_verification_warning < 2:
|
||||
# show warning
|
||||
__disable_certificate_verification_warning += 1
|
||||
logging.getLogger('TRAINS').warning(
|
||||
logging.getLogger('ClearML').warning(
|
||||
msg='InsecureRequestWarning: Certificate verification is disabled! Adding '
|
||||
'certificate verification is strongly advised. See: '
|
||||
'https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings')
|
||||
@@ -1,4 +1,3 @@
|
||||
from .defs import Environment
|
||||
from .config import Config, ConfigEntry
|
||||
from .errors import ConfigurationError
|
||||
from .environment import EnvEntry
|
||||
@@ -138,7 +138,7 @@ class Config(object):
|
||||
else:
|
||||
env_config_paths = []
|
||||
|
||||
env_config_path_override = os.environ.get(ENV_CONFIG_PATH_OVERRIDE_VAR)
|
||||
env_config_path_override = ENV_CONFIG_PATH_OVERRIDE_VAR.get()
|
||||
if env_config_path_override:
|
||||
env_config_paths = [expanduser(env_config_path_override)]
|
||||
|
||||
@@ -165,7 +165,7 @@ class Config(object):
|
||||
)
|
||||
|
||||
local_config_files = LOCAL_CONFIG_FILES
|
||||
local_config_override = os.environ.get(LOCAL_CONFIG_FILE_OVERRIDE_VAR)
|
||||
local_config_override = LOCAL_CONFIG_FILE_OVERRIDE_VAR.get()
|
||||
if local_config_override:
|
||||
local_config_files = [expanduser(local_config_override)]
|
||||
|
||||
@@ -1,6 +1,8 @@
|
||||
from os.path import expanduser
|
||||
from pathlib2 import Path
|
||||
|
||||
from ..backend_config.environment import EnvEntry
|
||||
|
||||
ENV_VAR = 'TRAINS_ENV'
|
||||
""" Name of system environment variable that can be used to specify the config environment name """
|
||||
|
||||
@@ -17,23 +19,24 @@ ENV_CONFIG_PATHS = [
|
||||
|
||||
|
||||
LOCAL_CONFIG_PATHS = [
|
||||
# '/etc/opt/trains', # used by servers for docker-generated configuration
|
||||
# expanduser('~/.trains/config'),
|
||||
# '/etc/opt/clearml', # used by servers for docker-generated configuration
|
||||
# expanduser('~/.clearml/config'),
|
||||
]
|
||||
""" Local config paths, not related to environment """
|
||||
|
||||
|
||||
LOCAL_CONFIG_FILES = [
|
||||
expanduser('~/trains.conf'), # used for workstation configuration (end-users, workers)
|
||||
expanduser('~/clearml.conf'), # used for workstation configuration (end-users, workers)
|
||||
]
|
||||
""" Local config files (not paths) """
|
||||
|
||||
|
||||
LOCAL_CONFIG_FILE_OVERRIDE_VAR = 'TRAINS_CONFIG_FILE'
|
||||
LOCAL_CONFIG_FILE_OVERRIDE_VAR = EnvEntry('CLEARML_CONFIG_FILE', 'TRAINS_CONFIG_FILE', )
|
||||
""" Local config file override environment variable. If this is set, no other local config files will be used. """
|
||||
|
||||
|
||||
ENV_CONFIG_PATH_OVERRIDE_VAR = 'TRAINS_CONFIG_PATH'
|
||||
ENV_CONFIG_PATH_OVERRIDE_VAR = EnvEntry('CLEARML_CONFIG_PATH', 'TRAINS_CONFIG_PATH', )
|
||||
"""
|
||||
Environment-related config path override environment variable. If this is set, no other env config path will be used.
|
||||
"""
|
||||
@@ -85,8 +85,9 @@ class Entry(object):
|
||||
|
||||
def set(self, value):
|
||||
# type: (Any, Any) -> (Text, Any)
|
||||
key, _ = self.get_pair(default=None, converter=None)
|
||||
self._set(key, str(value))
|
||||
# key, _ = self.get_pair(default=None, converter=None)
|
||||
for k in self.keys:
|
||||
self._set(k, str(value))
|
||||
|
||||
def _set(self, key, value):
|
||||
# type: (Text, Text) -> None
|
||||
64
clearml_agent/backend_config/environment.py
Normal file
64
clearml_agent/backend_config/environment.py
Normal file
@@ -0,0 +1,64 @@
|
||||
from os import getenv, environ
|
||||
|
||||
from .converters import text_to_bool
|
||||
from .entry import Entry, NotSet
|
||||
|
||||
|
||||
class EnvEntry(Entry):
|
||||
@classmethod
|
||||
def default_conversions(cls):
|
||||
conversions = super(EnvEntry, cls).default_conversions().copy()
|
||||
conversions[bool] = text_to_bool
|
||||
return conversions
|
||||
|
||||
def pop(self):
|
||||
for k in self.keys:
|
||||
environ.pop(k, None)
|
||||
|
||||
def _get(self, key):
|
||||
value = getenv(key, "").strip()
|
||||
return value or NotSet
|
||||
|
||||
def _set(self, key, value):
|
||||
environ[key] = value
|
||||
|
||||
def __str__(self):
|
||||
return "env:{}".format(super(EnvEntry, self).__str__())
|
||||
|
||||
def error(self, message):
|
||||
print("Environment configuration: {}".format(message))
|
||||
|
||||
|
||||
def backward_compatibility_support():
|
||||
from ..definitions import ENVIRONMENT_CONFIG, ENVIRONMENT_SDK_PARAMS, ENVIRONMENT_BACKWARD_COMPATIBLE
|
||||
if ENVIRONMENT_BACKWARD_COMPATIBLE.get():
|
||||
# Add TRAINS_ prefix on every CLEARML_ os environment we support
|
||||
for k, v in ENVIRONMENT_CONFIG.items():
|
||||
try:
|
||||
trains_vars = [var for var in v.vars if var.startswith('CLEARML_')]
|
||||
if not trains_vars:
|
||||
continue
|
||||
alg_var = trains_vars[0].replace('CLEARML_', 'TRAINS_', 1)
|
||||
if alg_var not in v.vars:
|
||||
v.vars = tuple(list(v.vars) + [alg_var])
|
||||
except:
|
||||
continue
|
||||
for k, v in ENVIRONMENT_SDK_PARAMS.items():
|
||||
try:
|
||||
trains_vars = [var for var in v if var.startswith('CLEARML_')]
|
||||
if not trains_vars:
|
||||
continue
|
||||
alg_var = trains_vars[0].replace('CLEARML_', 'TRAINS_', 1)
|
||||
if alg_var not in v:
|
||||
ENVIRONMENT_SDK_PARAMS[k] = tuple(list(v) + [alg_var])
|
||||
except:
|
||||
continue
|
||||
|
||||
# set OS environ:
|
||||
keys = environ.keys()
|
||||
for k in keys:
|
||||
if not k.startswith('CLEARML_'):
|
||||
continue
|
||||
backwards_k = k.replace('CLEARML_', 'TRAINS_', 1)
|
||||
if backwards_k not in keys:
|
||||
environ[backwards_k] = environ[k]
|
||||
@@ -4,11 +4,11 @@ from pathlib2 import Path
|
||||
|
||||
|
||||
def logger(path=None):
|
||||
name = "trains"
|
||||
name = "clearml"
|
||||
if path:
|
||||
p = Path(path)
|
||||
module = (p.parent if p.stem.startswith('_') else p).stem
|
||||
name = "trains.%s" % module
|
||||
name = "clearml.%s" % module
|
||||
return logging.getLogger(name)
|
||||
|
||||
|
||||
@@ -9,16 +9,16 @@ from operator import attrgetter
|
||||
from traceback import print_exc
|
||||
from typing import Text
|
||||
|
||||
from trains_agent.helper.console import ListFormatter, print_text
|
||||
from trains_agent.helper.dicts import filter_keys
|
||||
from clearml_agent.helper.console import ListFormatter, print_text
|
||||
from clearml_agent.helper.dicts import filter_keys
|
||||
|
||||
import six
|
||||
from trains_agent.backend_api import services
|
||||
from clearml_agent.backend_api import services
|
||||
|
||||
from trains_agent.errors import APIError, CommandFailedError
|
||||
from trains_agent.helper.base import Singleton, return_list, print_parameters, dump_yaml, load_yaml, error, warning
|
||||
from trains_agent.interface.base import ObjectID
|
||||
from trains_agent.session import Session
|
||||
from clearml_agent.errors import APIError, CommandFailedError
|
||||
from clearml_agent.helper.base import Singleton, return_list, print_parameters, dump_yaml, load_yaml, error, warning
|
||||
from clearml_agent.interface.base import ObjectID
|
||||
from clearml_agent.session import Session
|
||||
|
||||
|
||||
class NameResolutionError(CommandFailedError):
|
||||
@@ -74,7 +74,7 @@ class BaseCommandSection(object):
|
||||
|
||||
@staticmethod
|
||||
def log(message, *args):
|
||||
print("trains-agent: {}".format(message % args))
|
||||
print("clearml-agent: {}".format(message % args))
|
||||
|
||||
@classmethod
|
||||
def exit(cls, message, code=1): # type: (Text, int) -> ()
|
||||
@@ -1,4 +1,4 @@
|
||||
from trains_agent.commands.base import ServiceCommandSection
|
||||
from clearml_agent.commands.base import ServiceCommandSection
|
||||
|
||||
|
||||
class Config(ServiceCommandSection):
|
||||
@@ -5,13 +5,15 @@ from pyhocon import ConfigFactory, ConfigMissingException
|
||||
from pathlib2 import Path
|
||||
from six.moves.urllib.parse import urlparse
|
||||
|
||||
from trains_agent.backend_api.session import Session
|
||||
from trains_agent.backend_api.session.defs import ENV_HOST
|
||||
from trains_agent.backend_config.defs import LOCAL_CONFIG_FILES
|
||||
from clearml_agent.backend_api.session import Session
|
||||
from clearml_agent.backend_api.session.defs import ENV_HOST
|
||||
from clearml_agent.backend_config.defs import LOCAL_CONFIG_FILES
|
||||
|
||||
|
||||
description = """
|
||||
Please create new trains credentials through the profile page in your trains web app (e.g. https://demoapp.trains.allegro.ai/profile)
|
||||
Please create new clearml credentials through the profile page in your clearml web app (e.g. https://demoapp.demo.clear.ml/profile)
|
||||
Or with the free hosted service at https://app.community.clear.ml/profile
|
||||
|
||||
In the profile page, press "Create new credentials", then press "Copy to clipboard".
|
||||
|
||||
Paste copied configuration here:
|
||||
@@ -25,7 +27,7 @@ except Exception:
|
||||
|
||||
host_description = """
|
||||
Editing configuration file: {CONFIG_FILE}
|
||||
Enter the url of the trains-server's Web service, for example: {HOST}
|
||||
Enter the url of the clearml-server's Web service, for example: {HOST}
|
||||
""".format(
|
||||
CONFIG_FILE=LOCAL_CONFIG_FILES[0],
|
||||
HOST=def_host,
|
||||
@@ -33,8 +35,12 @@ Enter the url of the trains-server's Web service, for example: {HOST}
|
||||
|
||||
|
||||
def main():
|
||||
print('TRAINS-AGENT setup process')
|
||||
conf_file = Path(LOCAL_CONFIG_FILES[0]).absolute()
|
||||
print('CLEARML-AGENT setup process')
|
||||
for f in LOCAL_CONFIG_FILES:
|
||||
conf_file = Path(f).absolute()
|
||||
if conf_file.exists():
|
||||
break
|
||||
|
||||
if conf_file.exists() and conf_file.is_file() and conf_file.stat().st_size > 0:
|
||||
print('Configuration file already exists: {}'.format(str(conf_file)))
|
||||
print('Leaving setup, feel free to edit the configuration file.')
|
||||
@@ -42,7 +48,12 @@ def main():
|
||||
|
||||
print(description, end='')
|
||||
sentinel = ''
|
||||
parse_input = '\n'.join(iter(input, sentinel))
|
||||
parse_input = ''
|
||||
for line in iter(input, sentinel):
|
||||
parse_input += line+'\n'
|
||||
if line.rstrip() == '}':
|
||||
break
|
||||
|
||||
credentials = None
|
||||
api_server = None
|
||||
web_server = None
|
||||
@@ -86,7 +97,7 @@ def main():
|
||||
|
||||
files_host = input_url('File Store Host', files_host)
|
||||
|
||||
print('\nTRAINS Hosts configuration:\nWeb App: {}\nAPI: {}\nFile Store: {}\n'.format(
|
||||
print('\nClearML Hosts configuration:\nWeb App: {}\nAPI: {}\nFile Store: {}\n'.format(
|
||||
web_host, api_host, files_host))
|
||||
|
||||
retry = 1
|
||||
@@ -140,14 +151,14 @@ def main():
|
||||
# noinspection PyBroadException
|
||||
try:
|
||||
with open(str(conf_file), 'wt') as f:
|
||||
header = '# TRAINS-AGENT configuration file\n' \
|
||||
header = '# CLEARML-AGENT configuration file\n' \
|
||||
'api {\n' \
|
||||
' # Notice: \'host\' is the api server (default port 8008), not the web server.\n' \
|
||||
' api_server: %s\n' \
|
||||
' web_server: %s\n' \
|
||||
' files_server: %s\n' \
|
||||
' # Credentials are generated using the webapp, %s/profile\n' \
|
||||
' # Override with os environment: TRAINS_API_ACCESS_KEY / TRAINS_API_SECRET_KEY\n' \
|
||||
' # Override with os environment: CLEARML_API_ACCESS_KEY / CLEARML_API_SECRET_KEY\n' \
|
||||
' credentials {"access_key": "%s", "secret_key": "%s"}\n' \
|
||||
'}\n\n' % (api_host, web_host, files_host,
|
||||
web_host, credentials['access_key'], credentials['secret_key'])
|
||||
@@ -158,7 +169,7 @@ def main():
|
||||
'agent.git_pass=\"{}\"\n' \
|
||||
'\n'.format(git_user or '', git_pass or '')
|
||||
f.write(git_credentials)
|
||||
extra_index_str = '# extra_index_url: ["https://allegroai.jfrog.io/trainsai/api/pypi/public/simple"]\n' \
|
||||
extra_index_str = '# extra_index_url: ["https://allegroai.jfrog.io/clearml/api/pypi/public/simple"]\n' \
|
||||
'agent.package_manager.extra_index_url= ' \
|
||||
'[\n{}\n]\n\n'.format("\n".join(map("\"{}\"".format, extra_index_urls)))
|
||||
f.write(extra_index_str)
|
||||
@@ -168,7 +179,7 @@ def main():
|
||||
return
|
||||
|
||||
print('\nNew configuration stored in {}'.format(str(conf_file)))
|
||||
print('TRAINS-AGENT setup completed successfully.')
|
||||
print('CLEARML-AGENT setup completed successfully.')
|
||||
|
||||
|
||||
def parse_host(parsed_host, allow_input=True):
|
||||
@@ -309,7 +320,7 @@ def verify_url(parse_input):
|
||||
parsed_host = None
|
||||
except Exception:
|
||||
parsed_host = None
|
||||
print('Could not parse url {}\nEnter your trains-server host: '.format(parse_input), end='')
|
||||
print('Could not parse url {}\nEnter your clearml-server host: '.format(parse_input), end='')
|
||||
return parsed_host
|
||||
|
||||
|
||||
@@ -5,8 +5,8 @@ import time
|
||||
|
||||
from future.builtins import super
|
||||
|
||||
from trains_agent.commands.base import ServiceCommandSection
|
||||
from trains_agent.helper.base import return_list
|
||||
from clearml_agent.commands.base import ServiceCommandSection
|
||||
from clearml_agent.helper.base import return_list
|
||||
|
||||
|
||||
class Events(ServiceCommandSection):
|
||||
@@ -24,27 +24,28 @@ from typing import Text, Optional, Any, Tuple
|
||||
import attr
|
||||
import psutil
|
||||
import six
|
||||
from trains_agent.backend_api.services import queues as queues_api
|
||||
from trains_agent.backend_api.services import tasks as tasks_api
|
||||
from clearml_agent.backend_api.services import queues as queues_api
|
||||
from clearml_agent.backend_api.services import tasks as tasks_api
|
||||
from pathlib2 import Path
|
||||
from pyhocon import ConfigTree, ConfigFactory
|
||||
from six.moves.urllib.parse import quote
|
||||
|
||||
from trains_agent.backend_config.defs import UptimeConf
|
||||
from trains_agent.helper.check_update import start_check_update_daemon
|
||||
from trains_agent.commands.base import resolve_names, ServiceCommandSection
|
||||
from trains_agent.definitions import (
|
||||
from clearml_agent.backend_config.defs import UptimeConf
|
||||
from clearml_agent.helper.check_update import start_check_update_daemon
|
||||
from clearml_agent.commands.base import resolve_names, ServiceCommandSection
|
||||
from clearml_agent.definitions import (
|
||||
ENVIRONMENT_SDK_PARAMS,
|
||||
PROGRAM_NAME,
|
||||
DEFAULT_VENV_UPDATE_URL,
|
||||
ENV_DOCKER_IMAGE,
|
||||
ENV_TASK_EXECUTE_AS_USER,
|
||||
ENV_DOCKER_HOST_MOUNT,
|
||||
ENV_TASK_EXTRA_PYTHON_PATH,
|
||||
ENV_AGENT_GIT_USER,
|
||||
ENV_AGENT_GIT_PASS)
|
||||
from trains_agent.definitions import WORKING_REPOSITORY_DIR, PIP_EXTRA_INDICES
|
||||
from trains_agent.errors import APIError, CommandFailedError, Sigterm
|
||||
from trains_agent.helper.base import (
|
||||
ENV_AGENT_GIT_PASS, ENV_WORKER_ID, ENV_DOCKER_SKIP_GPUS_FLAG, )
|
||||
from clearml_agent.definitions import WORKING_REPOSITORY_DIR, PIP_EXTRA_INDICES
|
||||
from clearml_agent.errors import APIError, CommandFailedError, Sigterm
|
||||
from clearml_agent.helper.base import (
|
||||
return_list,
|
||||
print_parameters,
|
||||
dump_yaml,
|
||||
@@ -66,19 +67,19 @@ from trains_agent.helper.base import (
|
||||
is_linux_platform,
|
||||
rm_file,
|
||||
add_python_path, safe_remove_tree, )
|
||||
from trains_agent.helper.console import ensure_text, print_text, decode_binary_lines
|
||||
from trains_agent.helper.os.daemonize import daemonize_process
|
||||
from trains_agent.helper.package.base import PackageManager
|
||||
from trains_agent.helper.package.conda_api import CondaAPI
|
||||
from trains_agent.helper.package.post_req import PostRequirement
|
||||
from trains_agent.helper.package.external_req import ExternalRequirements
|
||||
from trains_agent.helper.package.pip_api.system import SystemPip
|
||||
from trains_agent.helper.package.pip_api.venv import VirtualenvPip
|
||||
from trains_agent.helper.package.poetry_api import PoetryConfig, PoetryAPI
|
||||
from trains_agent.helper.package.pytorch import PytorchRequirement
|
||||
from trains_agent.helper.package.requirements import RequirementsManager
|
||||
from trains_agent.helper.package.venv_update_api import VenvUpdateAPI
|
||||
from trains_agent.helper.process import (
|
||||
from clearml_agent.helper.console import ensure_text, print_text, decode_binary_lines
|
||||
from clearml_agent.helper.os.daemonize import daemonize_process
|
||||
from clearml_agent.helper.package.base import PackageManager
|
||||
from clearml_agent.helper.package.conda_api import CondaAPI
|
||||
from clearml_agent.helper.package.post_req import PostRequirement
|
||||
from clearml_agent.helper.package.external_req import ExternalRequirements
|
||||
from clearml_agent.helper.package.pip_api.system import SystemPip
|
||||
from clearml_agent.helper.package.pip_api.venv import VirtualenvPip
|
||||
from clearml_agent.helper.package.poetry_api import PoetryConfig, PoetryAPI
|
||||
from clearml_agent.helper.package.pytorch import PytorchRequirement
|
||||
from clearml_agent.helper.package.requirements import RequirementsManager
|
||||
from clearml_agent.helper.package.venv_update_api import VenvUpdateAPI
|
||||
from clearml_agent.helper.process import (
|
||||
kill_all_child_processes,
|
||||
WorkerParams,
|
||||
ExitStatus,
|
||||
@@ -90,17 +91,17 @@ from trains_agent.helper.process import (
|
||||
get_docker_id,
|
||||
commit_docker, terminate_process,
|
||||
)
|
||||
from trains_agent.helper.package.priority_req import PriorityPackageRequirement, PackageCollectorRequirement
|
||||
from trains_agent.helper.repo import clone_repository_cached, RepoInfo, VCS
|
||||
from trains_agent.helper.resource_monitor import ResourceMonitor
|
||||
from trains_agent.helper.runtime_verification import check_runtime, print_uptime_properties
|
||||
from trains_agent.session import Session
|
||||
from trains_agent.helper.singleton import Singleton
|
||||
from clearml_agent.helper.package.priority_req import PriorityPackageRequirement, PackageCollectorRequirement
|
||||
from clearml_agent.helper.repo import clone_repository_cached, RepoInfo, VCS
|
||||
from clearml_agent.helper.resource_monitor import ResourceMonitor
|
||||
from clearml_agent.helper.runtime_verification import check_runtime, print_uptime_properties
|
||||
from clearml_agent.session import Session
|
||||
from clearml_agent.helper.singleton import Singleton
|
||||
|
||||
from .events import Events
|
||||
|
||||
DOCKER_ROOT_CONF_FILE = "/root/trains.conf"
|
||||
DOCKER_DEFAULT_CONF_FILE = "/root/default_trains.conf"
|
||||
DOCKER_ROOT_CONF_FILE = "/root/clearml.conf"
|
||||
DOCKER_DEFAULT_CONF_FILE = "/root/default_clearml.conf"
|
||||
|
||||
|
||||
@attr.s
|
||||
@@ -337,8 +338,8 @@ class Worker(ServiceCommandSection):
|
||||
# last message before passing control to the actual task
|
||||
_task_logging_pass_control_message = "Running task id [{}]:"
|
||||
|
||||
_run_as_user_home = '/trains_agent_home'
|
||||
_docker_fixed_user_cache = '/trains_agent_cache'
|
||||
_run_as_user_home = '/clearml_agent_home'
|
||||
_docker_fixed_user_cache = '/clearml_agent_cache'
|
||||
_temp_cleanup_list = []
|
||||
|
||||
@property
|
||||
@@ -485,9 +486,9 @@ class Worker(ServiceCommandSection):
|
||||
return
|
||||
# setup console log
|
||||
temp_stdout_name = safe_mkstemp(
|
||||
suffix=".txt", prefix=".trains_agent_out.", name_only=True
|
||||
suffix=".txt", prefix=".clearml_agent_out.", name_only=True
|
||||
)
|
||||
# temp_stderr_name = safe_mkstemp(suffix=".txt", prefix=".trains_agent_err.", name_only=True)
|
||||
# temp_stderr_name = safe_mkstemp(suffix=".txt", prefix=".clearml_agent_err.", name_only=True)
|
||||
temp_stderr_name = None
|
||||
print(
|
||||
"Storing stdout and stderr log to '{}', '{}'".format(
|
||||
@@ -565,7 +566,7 @@ class Worker(ServiceCommandSection):
|
||||
status = ExitStatus.failure
|
||||
try:
|
||||
# set WORKER ID
|
||||
os.environ['TRAINS_WORKER_ID'] = self.worker_id
|
||||
ENV_WORKER_ID.set(self.worker_id)
|
||||
|
||||
if self._docker_force_pull and docker_image:
|
||||
full_pull_cmd = ['docker', 'pull', docker_image]
|
||||
@@ -619,7 +620,7 @@ class Worker(ServiceCommandSection):
|
||||
:param queues: IDs of queues to pull tasks from
|
||||
:type queues: list of ``Text``
|
||||
:param worker_params: Worker command line arguments
|
||||
:type worker_params: ``trains_agent.helper.process.WorkerParams``
|
||||
:type worker_params: ``clearml_agent.helper.process.WorkerParams``
|
||||
:param priority_order: If True pull order in priority manner. always from the first
|
||||
If False, pull from each queue once in a round robin manner
|
||||
:type priority_order: bool
|
||||
@@ -870,7 +871,7 @@ class Worker(ServiceCommandSection):
|
||||
|
||||
# create temp config file with current configuration
|
||||
self.temp_config_path = NamedTemporaryFile(
|
||||
suffix=".cfg", prefix=".trains_agent.", mode='w+t').name
|
||||
suffix=".cfg", prefix=".clearml_agent.", mode='w+t').name
|
||||
|
||||
# print docker image
|
||||
if docker is not False and docker is not None:
|
||||
@@ -893,19 +894,19 @@ class Worker(ServiceCommandSection):
|
||||
warning(message+'\n')
|
||||
|
||||
if self._services_mode:
|
||||
print('Trains-Agent running in services mode')
|
||||
print('ClearML-Agent running in services mode')
|
||||
|
||||
self._daemon_foreground = foreground
|
||||
if not foreground:
|
||||
out_file, name = safe_mkstemp(
|
||||
prefix=".trains_agent_daemon_out",
|
||||
prefix=".clearml_agent_daemon_out",
|
||||
suffix=".txt",
|
||||
open_kwargs={
|
||||
"buffering": self._session.config.get("agent.log_files_buffering", 1)
|
||||
},
|
||||
)
|
||||
print(
|
||||
"Running TRAINS-AGENT daemon in background mode, writing stdout/stderr to {}".format(
|
||||
"Running CLEARML-AGENT daemon in background mode, writing stdout/stderr to {}".format(
|
||||
name
|
||||
)
|
||||
)
|
||||
@@ -946,7 +947,7 @@ class Worker(ServiceCommandSection):
|
||||
tb = six.text_type(traceback.format_exc())
|
||||
print("FATAL ERROR:")
|
||||
print(tb)
|
||||
crash_file, name = safe_mkstemp(prefix=".trains_agent-crash", suffix=".log")
|
||||
crash_file, name = safe_mkstemp(prefix=".clearml_agent-crash", suffix=".log")
|
||||
try:
|
||||
with crash_file:
|
||||
crash_file.write(tb)
|
||||
@@ -1272,7 +1273,7 @@ class Worker(ServiceCommandSection):
|
||||
def _build_docker(self, docker, target, task_id, entry_point=None, standalone_mode=True):
|
||||
|
||||
self.temp_config_path = safe_mkstemp(
|
||||
suffix=".cfg", prefix=".trains_agent.", text=True, name_only=True
|
||||
suffix=".cfg", prefix=".clearml_agent.", text=True, name_only=True
|
||||
)
|
||||
if not target:
|
||||
target = "task_id_{}".format(task_id)
|
||||
@@ -1337,7 +1338,7 @@ class Worker(ServiceCommandSection):
|
||||
if entry_point == "clone_task" or entry_point == "reuse_task":
|
||||
change = 'ENTRYPOINT if [ ! -s "{trains_conf}" ] ; then ' \
|
||||
'cp {default_trains_conf} {trains_conf} ; ' \
|
||||
' fi ; trains-agent execute --id {task_id} --standalone-mode {clone}'.format(
|
||||
' fi ; clearml-agent execute --id {task_id} --standalone-mode {clone}'.format(
|
||||
default_trains_conf=DOCKER_DEFAULT_CONF_FILE,
|
||||
trains_conf=DOCKER_ROOT_CONF_FILE,
|
||||
task_id=task_id,
|
||||
@@ -1527,7 +1528,7 @@ class Worker(ServiceCommandSection):
|
||||
"task_id": current_task.id,
|
||||
"log_level": log_level,
|
||||
"log_to_backend": "0",
|
||||
"config_file": self._session.config_file, # The config file is the tmp file that trains_agent created
|
||||
"config_file": self._session.config_file, # The config file is the tmp file that clearml_agent created
|
||||
}
|
||||
os.environ.update(
|
||||
{
|
||||
@@ -1542,18 +1543,18 @@ class Worker(ServiceCommandSection):
|
||||
|
||||
# Add the script CWD to the python path
|
||||
python_path = get_python_path(script_dir, execution.entry_point, self.package_api, is_conda_env=self.is_conda)
|
||||
if os.environ.get(ENV_TASK_EXTRA_PYTHON_PATH):
|
||||
python_path = add_python_path(python_path, os.environ.get(ENV_TASK_EXTRA_PYTHON_PATH))
|
||||
if ENV_TASK_EXTRA_PYTHON_PATH.get():
|
||||
python_path = add_python_path(python_path, ENV_TASK_EXTRA_PYTHON_PATH.get())
|
||||
if python_path:
|
||||
os.environ['PYTHONPATH'] = python_path
|
||||
|
||||
# check if we want to run as another user, only supported on linux
|
||||
if os.environ.get(ENV_TASK_EXECUTE_AS_USER, None) and is_linux_platform():
|
||||
if ENV_TASK_EXECUTE_AS_USER.get() and is_linux_platform():
|
||||
command, script_dir = self._run_as_user_patch(
|
||||
command, self._session.config_file,
|
||||
script_dir, venv_folder,
|
||||
self._session.config.get('sdk.storage.cache.default_base_dir'),
|
||||
os.environ.get(ENV_TASK_EXECUTE_AS_USER))
|
||||
ENV_TASK_EXECUTE_AS_USER.get())
|
||||
use_execv = False
|
||||
else:
|
||||
use_execv = is_linux_platform() and not isinstance(self.package_api, (PoetryAPI, CondaAPI))
|
||||
@@ -1583,7 +1584,7 @@ class Worker(ServiceCommandSection):
|
||||
else:
|
||||
# store stdout/stderr into file, and send to backend
|
||||
temp_stdout_fname = log_file or safe_mkstemp(
|
||||
suffix=".txt", prefix=".trains_agent_out.", name_only=True
|
||||
suffix=".txt", prefix=".clearml_agent_out.", name_only=True
|
||||
)
|
||||
print("Storing stdout and stderr log into [%s]" % temp_stdout_fname)
|
||||
exit_code, _ = self._log_command_output(
|
||||
@@ -1966,7 +1967,7 @@ class Worker(ServiceCommandSection):
|
||||
|
||||
def debug(self, message):
|
||||
if self._session.debug_mode:
|
||||
print("trains_agent: {}".format(message))
|
||||
print("clearml_agent: {}".format(message))
|
||||
|
||||
def find_python_executable_for_version(self, config_version):
|
||||
# type: (Text) -> Tuple[Text, Text, Text]
|
||||
@@ -2166,7 +2167,7 @@ class Worker(ServiceCommandSection):
|
||||
args.update(kwargs)
|
||||
return self._get_docker_cmd(**args)
|
||||
|
||||
docker_image = str(os.environ.get("TRAINS_DOCKER_IMAGE") or
|
||||
docker_image = str(ENV_DOCKER_IMAGE.get() or
|
||||
self._session.config.get("agent.default_docker.image", "nvidia/cuda")) \
|
||||
if not docker_args else docker_args[0]
|
||||
docker_arguments = docker_image.split(' ') if docker_image else []
|
||||
@@ -2183,10 +2184,10 @@ class Worker(ServiceCommandSection):
|
||||
print("Running in Docker {} mode (v19.03 and above) - using default docker image: {} running {}\n".format(
|
||||
'*standalone*' if self._standalone_mode else '', docker_image, python_version))
|
||||
temp_config = deepcopy(self._session.config)
|
||||
mounted_cache_dir = self._docker_fixed_user_cache # '/root/.trains/cache'
|
||||
mounted_pip_dl_dir = '/root/.trains/pip-download-cache'
|
||||
mounted_vcs_cache = '/root/.trains/vcs-cache'
|
||||
mounted_venv_dir = '/root/.trains/venvs-builds'
|
||||
mounted_cache_dir = self._docker_fixed_user_cache # '/root/.clearml/cache'
|
||||
mounted_pip_dl_dir = '/root/.clearml/pip-download-cache'
|
||||
mounted_vcs_cache = '/root/.clearml/vcs-cache'
|
||||
mounted_venv_dir = '/root/.clearml/venvs-builds'
|
||||
host_cache = Path(os.path.expandvars(
|
||||
self._session.config["sdk.storage.cache.default_base_dir"])).expanduser().as_posix()
|
||||
host_pip_dl = Path(os.path.expandvars(
|
||||
@@ -2209,10 +2210,10 @@ class Worker(ServiceCommandSection):
|
||||
self._session.config.get("agent.git_pass", None)))
|
||||
|
||||
host_apt_cache = Path(os.path.expandvars(self._session.config.get(
|
||||
"agent.docker_apt_cache", '~/.trains/apt-cache'))).expanduser().as_posix()
|
||||
"agent.docker_apt_cache", '~/.clearml/apt-cache'))).expanduser().as_posix()
|
||||
host_pip_cache = Path(os.path.expandvars(self._session.config.get(
|
||||
"agent.docker_pip_cache", '~/.trains/pip-cache'))).expanduser().as_posix()
|
||||
host_ssh_cache = mkdtemp(prefix='trains_agent.ssh.')
|
||||
"agent.docker_pip_cache", '~/.clearml/pip-cache'))).expanduser().as_posix()
|
||||
host_ssh_cache = mkdtemp(prefix='clearml_agent.ssh.')
|
||||
self._temp_cleanup_list.append(host_ssh_cache)
|
||||
|
||||
# make sure all folders are valid
|
||||
@@ -2257,7 +2258,7 @@ class Worker(ServiceCommandSection):
|
||||
preprocess_bash_script = self._session.config.get("agent.docker_preprocess_bash_script", None)
|
||||
|
||||
self.temp_config_path = self.temp_config_path or safe_mkstemp(
|
||||
suffix=".cfg", prefix=".trains_agent.", text=True, name_only=True
|
||||
suffix=".cfg", prefix=".clearml_agent.", text=True, name_only=True
|
||||
)
|
||||
|
||||
docker_cmd = dict(worker_id=self.worker_id,
|
||||
@@ -2298,13 +2299,13 @@ class Worker(ServiceCommandSection):
|
||||
dockers_nvidia_visible_devices = 'all'
|
||||
gpu_devices = os.environ.get('NVIDIA_VISIBLE_DEVICES', None)
|
||||
if gpu_devices is None or gpu_devices.lower().strip() == 'all':
|
||||
if os.environ.get('TRAINS_DOCKER_SKIP_GPUS_FLAG', None):
|
||||
if ENV_DOCKER_SKIP_GPUS_FLAG.get():
|
||||
dockers_nvidia_visible_devices = os.environ.get('NVIDIA_VISIBLE_DEVICES') or \
|
||||
dockers_nvidia_visible_devices
|
||||
else:
|
||||
base_cmd += ['--gpus', 'all', ]
|
||||
elif gpu_devices.strip() and gpu_devices.strip() != 'none':
|
||||
if os.environ.get('TRAINS_DOCKER_SKIP_GPUS_FLAG', None):
|
||||
if ENV_DOCKER_SKIP_GPUS_FLAG.get():
|
||||
dockers_nvidia_visible_devices = gpu_devices
|
||||
else:
|
||||
base_cmd += ['--gpus', '\"device={}\"'.format(gpu_devices), ]
|
||||
@@ -2338,7 +2339,7 @@ class Worker(ServiceCommandSection):
|
||||
|
||||
# check if we need to map host folders
|
||||
if ENV_DOCKER_HOST_MOUNT.get():
|
||||
# expect TRAINS_AGENT_K8S_HOST_MOUNT = '/mnt/host/data:/root/.trains'
|
||||
# expect CLEARML_AGENT_K8S_HOST_MOUNT = '/mnt/host/data:/root/.clearml'
|
||||
k8s_node_mnt, _, k8s_pod_mnt = ENV_DOCKER_HOST_MOUNT.get().partition(':')
|
||||
# search and replace all the host folders with the k8s
|
||||
host_mounts = [host_apt_cache, host_pip_cache, host_pip_dl, host_cache, host_vcs_cache]
|
||||
@@ -2351,7 +2352,7 @@ class Worker(ServiceCommandSection):
|
||||
host_apt_cache, host_pip_cache, host_pip_dl, host_cache, host_vcs_cache = host_mounts
|
||||
|
||||
# copy the configuration file into the mounted folder
|
||||
new_conf_file = os.path.join(k8s_pod_mnt, '.trains_agent.{}.cfg'.format(quote(worker_id, safe="")))
|
||||
new_conf_file = os.path.join(k8s_pod_mnt, '.clearml_agent.{}.cfg'.format(quote(worker_id, safe="")))
|
||||
try:
|
||||
rm_tree(new_conf_file)
|
||||
rm_file(new_conf_file)
|
||||
@@ -2361,7 +2362,7 @@ class Worker(ServiceCommandSection):
|
||||
raise ValueError('Error: could not copy configuration file into: {}'.format(new_conf_file))
|
||||
|
||||
if host_ssh_cache:
|
||||
new_ssh_cache = os.path.join(k8s_pod_mnt, '.trains_agent.{}.ssh'.format(quote(worker_id, safe="")))
|
||||
new_ssh_cache = os.path.join(k8s_pod_mnt, '.clearml_agent.{}.ssh'.format(quote(worker_id, safe="")))
|
||||
try:
|
||||
rm_tree(new_ssh_cache)
|
||||
shutil.copytree(host_ssh_cache, new_ssh_cache)
|
||||
@@ -2369,29 +2370,29 @@ class Worker(ServiceCommandSection):
|
||||
except Exception:
|
||||
raise ValueError('Error: could not copy .ssh directory into: {}'.format(new_ssh_cache))
|
||||
|
||||
base_cmd += ['-e', 'TRAINS_WORKER_ID='+worker_id, ]
|
||||
base_cmd += ['-e', 'CLEARML_WORKER_ID='+worker_id, ]
|
||||
# update the docker image, so the system knows where it runs
|
||||
base_cmd += ['-e', 'TRAINS_DOCKER_IMAGE={} {}'.format(docker_image, ' '.join(docker_arguments)).strip()]
|
||||
base_cmd += ['-e', 'CLEARML_DOCKER_IMAGE={} {}'.format(docker_image, ' '.join(docker_arguments)).strip()]
|
||||
|
||||
# if we are running a RC version, install the same version in the docker
|
||||
# because the default latest, will be a release version (not RC)
|
||||
specify_version = ''
|
||||
try:
|
||||
from trains_agent.version import __version__
|
||||
from clearml_agent.version import __version__
|
||||
_version_parts = __version__.split('.')
|
||||
if force_current_version or 'rc' in _version_parts[-1].lower() or 'rc' in _version_parts[-2].lower():
|
||||
specify_version = '=={}'.format(__version__)
|
||||
except:
|
||||
pass
|
||||
|
||||
if os.environ.get('FORCE_LOCAL_TRAINS_AGENT_WHEEL'):
|
||||
local_wheel = os.path.expanduser(os.environ.get('FORCE_LOCAL_TRAINS_AGENT_WHEEL'))
|
||||
if os.environ.get('FORCE_LOCAL_CLEARML_AGENT_WHEEL'):
|
||||
local_wheel = os.path.expanduser(os.environ.get('FORCE_LOCAL_CLEARML_AGENT_WHEEL'))
|
||||
docker_wheel = str(Path('/tmp') / Path(local_wheel).name)
|
||||
base_cmd += ['-v', local_wheel + ':' + docker_wheel]
|
||||
trains_agent_wheel = '\"{}\"'.format(docker_wheel)
|
||||
clearml_agent_wheel = '\"{}\"'.format(docker_wheel)
|
||||
else:
|
||||
# trains-agent{specify_version}
|
||||
trains_agent_wheel = 'trains-agent{specify_version}'.format(specify_version=specify_version)
|
||||
# clearml-agent{specify_version}
|
||||
clearml_agent_wheel = 'clearml-agent{specify_version}'.format(specify_version=specify_version)
|
||||
|
||||
if not standalone_mode:
|
||||
if not bash_script:
|
||||
@@ -2421,10 +2422,10 @@ class Worker(ServiceCommandSection):
|
||||
docker_bash_script + " ; " +
|
||||
"[ ! -z $LOCAL_PYTHON ] || export LOCAL_PYTHON={python} ; " +
|
||||
"$LOCAL_PYTHON -m pip install -U \"pip{pip_version}\" ; " +
|
||||
"$LOCAL_PYTHON -m pip install -U {trains_agent_wheel} ; ").format(
|
||||
"$LOCAL_PYTHON -m pip install -U {clearml_agent_wheel} ; ").format(
|
||||
python_single_digit=python_version.split('.')[0],
|
||||
python=python_version, pip_version=PackageManager.get_pip_version(),
|
||||
trains_agent_wheel=trains_agent_wheel)
|
||||
clearml_agent_wheel=clearml_agent_wheel)
|
||||
|
||||
if host_git_credentials:
|
||||
for git_credentials in host_git_credentials:
|
||||
@@ -2442,7 +2443,7 @@ class Worker(ServiceCommandSection):
|
||||
update_scheme +
|
||||
extra_shell_script +
|
||||
"cp {} {} ; ".format(DOCKER_ROOT_CONF_FILE, DOCKER_DEFAULT_CONF_FILE) +
|
||||
"NVIDIA_VISIBLE_DEVICES={nv_visible} $LOCAL_PYTHON -u -m trains_agent ".format(
|
||||
"NVIDIA_VISIBLE_DEVICES={nv_visible} $LOCAL_PYTHON -u -m clearml_agent ".format(
|
||||
nv_visible=dockers_nvidia_visible_devices, python=python_version)
|
||||
])
|
||||
|
||||
@@ -2473,7 +2474,7 @@ class Worker(ServiceCommandSection):
|
||||
os.setuid(self.uid)
|
||||
|
||||
# create a home folder for our user
|
||||
trains_agent_home = self._run_as_user_home + '{}'.format(
|
||||
clearml_agent_home = self._run_as_user_home + '{}'.format(
|
||||
'.'+str(Singleton.get_slot()) if Singleton.get_slot() else '')
|
||||
try:
|
||||
home_folder = self._run_as_user_home
|
||||
@@ -2511,7 +2512,7 @@ class Worker(ServiceCommandSection):
|
||||
|
||||
# make sure we could access the trains_conf file
|
||||
try:
|
||||
user_trains_conf = os.path.join(home_folder, 'trains.conf')
|
||||
user_trains_conf = os.path.join(home_folder, 'clearml.conf')
|
||||
shutil.copy(trains_conf, user_trains_conf)
|
||||
Path(user_trains_conf).chmod(0o0777)
|
||||
except:
|
||||
@@ -2539,11 +2540,11 @@ class Worker(ServiceCommandSection):
|
||||
(worker_id and uid == worker_id) or
|
||||
(not worker_id and uid.startswith('{}:'.format(worker_name)))):
|
||||
# this is us kill it
|
||||
print('Terminating trains-agent worker_id={} pid={}'.format(uid, pid))
|
||||
print('Terminating clearml-agent worker_id={} pid={}'.format(uid, pid))
|
||||
if not terminate_process(pid, timeout=10):
|
||||
error('Could not terminate process pid={}'.format(pid))
|
||||
return True
|
||||
print('Could not find a running trains-agent instance with worker_name={} worker_id={}'.format(
|
||||
print('Could not find a running clearml-agent instance with worker_name={} worker_id={}'.format(
|
||||
worker_name, worker_id))
|
||||
return False
|
||||
|
||||
@@ -1,8 +1,8 @@
|
||||
"""
|
||||
Script for generating command-line completion.
|
||||
Called by trains_agent/utilities/complete.sh (or a copy of it) like so:
|
||||
Called by clearml_agent/utilities/complete.sh (or a copy of it) like so:
|
||||
|
||||
python -m trains_agent.complete "current command line"
|
||||
python -m clearml_agent.complete "current command line"
|
||||
|
||||
And writes line-separated completion targets to stdout.
|
||||
Results are line-separated in order to enable other whitespace in results.
|
||||
@@ -13,7 +13,7 @@ from __future__ import print_function
|
||||
import argparse
|
||||
import sys
|
||||
|
||||
from trains_agent.interface import get_parser
|
||||
from clearml_agent.interface import get_parser
|
||||
|
||||
|
||||
def is_argument_required(action):
|
||||
@@ -1,7 +1,7 @@
|
||||
from pyhocon import ConfigTree
|
||||
|
||||
import six
|
||||
from trains_agent.helper.base import Singleton
|
||||
from clearml_agent.helper.base import Singleton
|
||||
|
||||
|
||||
@six.add_metaclass(Singleton)
|
||||
@@ -1,22 +1,22 @@
|
||||
from datetime import timedelta
|
||||
from distutils.util import strtobool
|
||||
from enum import IntEnum
|
||||
from os import getenv
|
||||
from os import getenv, environ
|
||||
from typing import Text, Optional, Union, Tuple, Any
|
||||
|
||||
from furl import furl
|
||||
from pathlib2 import Path
|
||||
|
||||
import six
|
||||
from trains_agent.helper.base import normalize_path
|
||||
from clearml_agent.helper.base import normalize_path
|
||||
|
||||
PROGRAM_NAME = "trains-agent"
|
||||
PROGRAM_NAME = "clearml-agent"
|
||||
FROM_FILE_PREFIX_CHARS = "@"
|
||||
|
||||
CONFIG_DIR = normalize_path("~/.trains")
|
||||
TOKEN_CACHE_FILE = normalize_path("~/.trains.trains_agent.tmp")
|
||||
CONFIG_DIR = normalize_path("~/.clearml")
|
||||
TOKEN_CACHE_FILE = normalize_path("~/.clearml.clearml_agent.tmp")
|
||||
|
||||
CONFIG_FILE_CANDIDATES = ["~/trains.conf"]
|
||||
CONFIG_FILE_CANDIDATES = ["~/clearml.conf"]
|
||||
|
||||
|
||||
def find_config_path():
|
||||
@@ -40,6 +40,14 @@ class EnvironmentConfig(object):
|
||||
self.vars = names
|
||||
self.type = kwargs.pop("type", six.text_type)
|
||||
|
||||
def pop(self):
|
||||
for k in self.vars:
|
||||
environ.pop(k, None)
|
||||
|
||||
def set(self, value):
|
||||
for k in self.vars:
|
||||
environ[k] = str(value)
|
||||
|
||||
def convert(self, value):
|
||||
return self.conversions.get(self.type, self.type)(value)
|
||||
|
||||
@@ -55,23 +63,23 @@ class EnvironmentConfig(object):
|
||||
|
||||
|
||||
ENVIRONMENT_CONFIG = {
|
||||
"api.api_server": EnvironmentConfig("TRAINS_API_HOST", ),
|
||||
"api.api_server": EnvironmentConfig("CLEARML_API_HOST", "TRAINS_API_HOST", ),
|
||||
"api.credentials.access_key": EnvironmentConfig(
|
||||
"TRAINS_API_ACCESS_KEY",
|
||||
"CLEARML_API_ACCESS_KEY", "TRAINS_API_ACCESS_KEY",
|
||||
),
|
||||
"api.credentials.secret_key": EnvironmentConfig(
|
||||
"TRAINS_API_SECRET_KEY",
|
||||
"CLEARML_API_SECRET_KEY", "TRAINS_API_SECRET_KEY",
|
||||
),
|
||||
"agent.worker_name": EnvironmentConfig("TRAINS_WORKER_NAME", ),
|
||||
"agent.worker_id": EnvironmentConfig("TRAINS_WORKER_ID", ),
|
||||
"agent.worker_name": EnvironmentConfig("CLEARML_WORKER_NAME", "TRAINS_WORKER_NAME", ),
|
||||
"agent.worker_id": EnvironmentConfig("CLEARML_WORKER_ID", "TRAINS_WORKER_ID", ),
|
||||
"agent.cuda_version": EnvironmentConfig(
|
||||
"TRAINS_CUDA_VERSION", "CUDA_VERSION"
|
||||
"CLEARML_CUDA_VERSION", "TRAINS_CUDA_VERSION", "CUDA_VERSION"
|
||||
),
|
||||
"agent.cudnn_version": EnvironmentConfig(
|
||||
"TRAINS_CUDNN_VERSION", "CUDNN_VERSION"
|
||||
"CLEARML_CUDNN_VERSION", "TRAINS_CUDNN_VERSION", "CUDNN_VERSION"
|
||||
),
|
||||
"agent.cpu_only": EnvironmentConfig(
|
||||
"TRAINS_CPU_ONLY", "CPU_ONLY", type=bool
|
||||
names=("CLEARML_CPU_ONLY", "TRAINS_CPU_ONLY", "CPU_ONLY"), type=bool
|
||||
),
|
||||
"sdk.aws.s3.key": EnvironmentConfig("AWS_ACCESS_KEY_ID"),
|
||||
"sdk.aws.s3.secret": EnvironmentConfig("AWS_SECRET_ACCESS_KEY"),
|
||||
@@ -82,13 +90,14 @@ ENVIRONMENT_CONFIG = {
|
||||
}
|
||||
|
||||
ENVIRONMENT_SDK_PARAMS = {
|
||||
"task_id": ("TRAINS_TASK_ID", ),
|
||||
"config_file": ("TRAINS_CONFIG_FILE", ),
|
||||
"log_level": ("TRAINS_LOG_LEVEL", ),
|
||||
"log_to_backend": ("TRAINS_LOG_TASK_TO_BACKEND", ),
|
||||
"task_id": ("CLEARML_TASK_ID", "TRAINS_TASK_ID", ),
|
||||
"config_file": ("CLEARML_CONFIG_FILE", "TRAINS_CONFIG_FILE", ),
|
||||
"log_level": ("CLEARML_LOG_LEVEL", "TRAINS_LOG_LEVEL", ),
|
||||
"log_to_backend": ("CLEARML_LOG_TASK_TO_BACKEND", "TRAINS_LOG_TASK_TO_BACKEND", ),
|
||||
}
|
||||
|
||||
ENVIRONMENT_BACKWARD_COMPATIBLE = EnvironmentConfig("TRAINS_AGENT_ALG_ENV", type=bool)
|
||||
ENVIRONMENT_BACKWARD_COMPATIBLE = EnvironmentConfig(
|
||||
names=("CLEARML_AGENT_ALG_ENV", "TRAINS_AGENT_ALG_ENV"), type=bool)
|
||||
|
||||
VIRTUAL_ENVIRONMENT_PATH = {
|
||||
"python2": normalize_path(CONFIG_DIR, "py2venv"),
|
||||
@@ -96,7 +105,7 @@ VIRTUAL_ENVIRONMENT_PATH = {
|
||||
}
|
||||
|
||||
DEFAULT_BASE_DIR = normalize_path(CONFIG_DIR, "data_cache")
|
||||
DEFAULT_HOST = "https://demoapi.trains.allegro.ai"
|
||||
DEFAULT_HOST = "https://demoapi.demo.clear.ml"
|
||||
MAX_DATASET_SOURCES_COUNT = 50000
|
||||
|
||||
INVALID_WORKER_ID = (400, 1001)
|
||||
@@ -105,11 +114,6 @@ WORKER_ALREADY_REGISTERED = (400, 1003)
|
||||
API_VERSION = "v1.5"
|
||||
TOKEN_EXPIRATION_SECONDS = int(timedelta(days=2).total_seconds())
|
||||
|
||||
HTTP_HEADERS = {
|
||||
"worker": "X-Trains-Worker",
|
||||
"act-as": "X-Trains-Act-As",
|
||||
"client": "X-Trains-Agent",
|
||||
}
|
||||
METADATA_EXTENSION = ".json"
|
||||
|
||||
DEFAULT_VENV_UPDATE_URL = (
|
||||
@@ -120,12 +124,16 @@ DEFAULT_VCS_CACHE = normalize_path(CONFIG_DIR, "vcs-cache")
|
||||
PIP_EXTRA_INDICES = [
|
||||
]
|
||||
DEFAULT_PIP_DOWNLOAD_CACHE = normalize_path(CONFIG_DIR, "pip-download-cache")
|
||||
ENV_AGENT_GIT_USER = EnvironmentConfig('TRAINS_AGENT_GIT_USER')
|
||||
ENV_AGENT_GIT_PASS = EnvironmentConfig('TRAINS_AGENT_GIT_PASS')
|
||||
ENV_AGENT_GIT_HOST = EnvironmentConfig('TRAINS_AGENT_GIT_HOST')
|
||||
ENV_TASK_EXECUTE_AS_USER = 'TRAINS_AGENT_EXEC_USER'
|
||||
ENV_TASK_EXTRA_PYTHON_PATH = 'TRAINS_AGENT_EXTRA_PYTHON_PATH'
|
||||
ENV_DOCKER_HOST_MOUNT = EnvironmentConfig('TRAINS_AGENT_K8S_HOST_MOUNT', 'TRAINS_AGENT_DOCKER_HOST_MOUNT')
|
||||
ENV_DOCKER_IMAGE = EnvironmentConfig('CLEARML_DOCKER_IMAGE', 'TRAINS_DOCKER_IMAGE')
|
||||
ENV_WORKER_ID = EnvironmentConfig('CLEARML_WORKER_ID', 'TRAINS_WORKER_ID')
|
||||
ENV_DOCKER_SKIP_GPUS_FLAG = EnvironmentConfig('CLEARML_DOCKER_SKIP_GPUS_FLAG', 'TRAINS_DOCKER_SKIP_GPUS_FLAG')
|
||||
ENV_AGENT_GIT_USER = EnvironmentConfig('CLEARML_AGENT_GIT_USER', 'TRAINS_AGENT_GIT_USER')
|
||||
ENV_AGENT_GIT_PASS = EnvironmentConfig('CLEARML_AGENT_GIT_PASS', 'TRAINS_AGENT_GIT_PASS')
|
||||
ENV_AGENT_GIT_HOST = EnvironmentConfig('CLEARML_AGENT_GIT_HOST', 'TRAINS_AGENT_GIT_HOST')
|
||||
ENV_TASK_EXECUTE_AS_USER = EnvironmentConfig('CLEARML_AGENT_EXEC_USER', 'TRAINS_AGENT_EXEC_USER')
|
||||
ENV_TASK_EXTRA_PYTHON_PATH = EnvironmentConfig('CLEARML_AGENT_EXTRA_PYTHON_PATH', 'TRAINS_AGENT_EXTRA_PYTHON_PATH')
|
||||
ENV_DOCKER_HOST_MOUNT = EnvironmentConfig('CLEARML_AGENT_K8S_HOST_MOUNT', 'CLEARML_AGENT_DOCKER_HOST_MOUNT',
|
||||
'TRAINS_AGENT_K8S_HOST_MOUNT', 'TRAINS_AGENT_DOCKER_HOST_MOUNT')
|
||||
|
||||
|
||||
class FileBuffering(IntEnum):
|
||||
@@ -30,7 +30,7 @@ LOCAL_REGEX = re.compile(
|
||||
|
||||
class Requirement(object):
|
||||
"""
|
||||
Represents a single requirementfrom trains_agent.external.requirements_parser.requirement import Requirement
|
||||
Represents a single requirementfrom clearml_agent.external.requirements_parser.requirement import Requirement
|
||||
|
||||
Typically instances of this class are created with ``Requirement.parse``.
|
||||
For local file requirements, there's no verification that the file
|
||||
@@ -3,6 +3,7 @@ from __future__ import print_function, division, unicode_literals
|
||||
import base64
|
||||
import logging
|
||||
import os
|
||||
import re
|
||||
import subprocess
|
||||
import tempfile
|
||||
from copy import deepcopy
|
||||
@@ -12,14 +13,15 @@ import json
|
||||
from time import sleep
|
||||
from typing import Text, List
|
||||
|
||||
from trains_agent.commands.events import Events
|
||||
from trains_agent.commands.worker import Worker
|
||||
from trains_agent.errors import APIError
|
||||
from trains_agent.helper.base import safe_remove_file
|
||||
from trains_agent.helper.dicts import merge_dicts
|
||||
from trains_agent.helper.process import get_bash_output
|
||||
from trains_agent.helper.resource_monitor import ResourceMonitor
|
||||
from trains_agent.interface.base import ObjectID
|
||||
from clearml_agent.commands.events import Events
|
||||
from clearml_agent.commands.worker import Worker
|
||||
from clearml_agent.definitions import ENV_DOCKER_IMAGE
|
||||
from clearml_agent.errors import APIError
|
||||
from clearml_agent.helper.base import safe_remove_file
|
||||
from clearml_agent.helper.dicts import merge_dicts
|
||||
from clearml_agent.helper.process import get_bash_output
|
||||
from clearml_agent.helper.resource_monitor import ResourceMonitor
|
||||
from clearml_agent.interface.base import ObjectID
|
||||
|
||||
|
||||
class K8sIntegration(Worker):
|
||||
@@ -27,16 +29,16 @@ class K8sIntegration(Worker):
|
||||
|
||||
KUBECTL_APPLY_CMD = "kubectl apply -f"
|
||||
|
||||
KUBECTL_RUN_CMD = "kubectl run trains-id-{task_id} " \
|
||||
KUBECTL_RUN_CMD = "kubectl run clearml-{queue_name}-id-{task_id} " \
|
||||
"--image {docker_image} " \
|
||||
"--restart=Never --replicas=1 " \
|
||||
"--generator=run-pod/v1 " \
|
||||
"--namespace=trains"
|
||||
"--namespace=clearml"
|
||||
|
||||
KUBECTL_DELETE_CMD = "kubectl delete pods " \
|
||||
"--selector=TRAINS=agent " \
|
||||
"--field-selector=status.phase!=Pending,status.phase!=Running " \
|
||||
"--namespace=trains"
|
||||
"--namespace=clearml"
|
||||
|
||||
BASH_INSTALL_SSH_CMD = [
|
||||
"apt-get install -y openssh-server",
|
||||
@@ -45,7 +47,8 @@ class K8sIntegration(Worker):
|
||||
"echo 'PermitRootLogin yes' >> /etc/ssh/sshd_config",
|
||||
"sed -i 's/PermitRootLogin prohibit-password/PermitRootLogin yes/' /etc/ssh/sshd_config",
|
||||
r"sed 's@session\s*required\s*pam_loginuid.so@session optional pam_loginuid.so@g' -i /etc/pam.d/sshd",
|
||||
"echo 'AcceptEnv TRAINS_API_ACCESS_KEY TRAINS_API_SECRET_KEY' >> /etc/ssh/sshd_config",
|
||||
"echo 'AcceptEnv TRAINS_API_ACCESS_KEY TRAINS_API_SECRET_KEY CLEARML_API_ACCESS_KEY CLEARML_API_SECRET_KEY' "
|
||||
">> /etc/ssh/sshd_config",
|
||||
'echo "export VISIBLE=now" >> /etc/profile',
|
||||
'echo "export PATH=$PATH" >> /etc/profile',
|
||||
'echo "ldconfig" >> /etc/profile',
|
||||
@@ -62,9 +65,9 @@ class K8sIntegration(Worker):
|
||||
"export LOCAL_PYTHON=$(which python3.$i) && break ; done",
|
||||
"[ ! -z $LOCAL_PYTHON ] || apt-get install -y python3-pip",
|
||||
"[ ! -z $LOCAL_PYTHON ] || export LOCAL_PYTHON=python3",
|
||||
"$LOCAL_PYTHON -m pip install trains-agent",
|
||||
"$LOCAL_PYTHON -m pip install clearml-agent",
|
||||
"{extra_bash_init_cmd}",
|
||||
"$LOCAL_PYTHON -m trains_agent execute --full-monitoring --require-queue --id {task_id}"
|
||||
"$LOCAL_PYTHON -m clearml_agent execute --full-monitoring --require-queue --id {task_id}"
|
||||
]
|
||||
|
||||
AGENT_LABEL = "TRAINS=agent"
|
||||
@@ -107,7 +110,7 @@ class K8sIntegration(Worker):
|
||||
:param str overrides_yaml: YAML file containing the overrides for the pod (optional)
|
||||
:param str template_yaml: YAML file containing the template for the pod (optional).
|
||||
If provided the pod is scheduled with kubectl apply and overrides are ignored, otherwise with kubectl run.
|
||||
:param str trains_conf_file: trains.conf file to be use by the pod itself (optional)
|
||||
:param str trains_conf_file: clearml.conf file to be use by the pod itself (optional)
|
||||
:param str extra_bash_init_script: Additional bash script to run before starting the Task inside the container
|
||||
"""
|
||||
super(K8sIntegration, self).__init__()
|
||||
@@ -212,7 +215,7 @@ class K8sIntegration(Worker):
|
||||
if task_data.execution.docker_cmd:
|
||||
docker_parts = task_data.execution.docker_cmd
|
||||
else:
|
||||
docker_parts = str(os.environ.get("TRAINS_DOCKER_IMAGE") or
|
||||
docker_parts = str(ENV_DOCKER_IMAGE.get() or
|
||||
self._session.config.get("agent.default_docker.image", "nvidia/cuda"))
|
||||
|
||||
# take the first part, this is the docker image name (not arguments)
|
||||
@@ -220,10 +223,10 @@ class K8sIntegration(Worker):
|
||||
docker_image = docker_parts[0]
|
||||
docker_args = docker_parts[1:] if len(docker_parts) > 1 else []
|
||||
|
||||
# get the trains.conf encoded file
|
||||
# get the clearml.conf encoded file
|
||||
# noinspection PyProtectedMember
|
||||
hocon_config_encoded = (self.trains_conf_file or self._session._config_file).encode('ascii')
|
||||
create_trains_conf = "echo '{}' | base64 --decode >> ~/trains.conf".format(
|
||||
create_trains_conf = "echo '{}' | base64 --decode >> ~/clearml.conf".format(
|
||||
base64.b64encode(
|
||||
hocon_config_encoded
|
||||
).decode('ascii')
|
||||
@@ -232,10 +235,20 @@ class K8sIntegration(Worker):
|
||||
if self.ports_mode:
|
||||
print("Kubernetes looking for available pod to use")
|
||||
|
||||
# noinspection PyBroadException
|
||||
try:
|
||||
queue_name = self._session.api_client.queues.get_by_id(queue=queue).name
|
||||
except Exception:
|
||||
queue_name = 'k8s'
|
||||
|
||||
# conform queue name to k8s standards
|
||||
safe_queue_name = queue_name.lower().strip()
|
||||
safe_queue_name = re.sub(r'\W+', '', safe_queue_name).replace('_', '').replace('-', '')
|
||||
|
||||
# Search for a free pod number
|
||||
pod_number = 1
|
||||
while self.ports_mode:
|
||||
kubectl_cmd_new = "kubectl get pods -l {pod_label},{agent_label} -n trains".format(
|
||||
kubectl_cmd_new = "kubectl get pods -l {pod_label},{agent_label} -n clearml".format(
|
||||
pod_label=self.LIMIT_POD_LABEL.format(pod_number=pod_number),
|
||||
agent_label=self.AGENT_LABEL
|
||||
)
|
||||
@@ -256,7 +269,8 @@ class K8sIntegration(Worker):
|
||||
)
|
||||
)
|
||||
self._session.api_client.tasks.reset(task_id)
|
||||
self._session.api_client.tasks.enqueue(task_id, queue=queue)
|
||||
self._session.api_client.tasks.enqueue(
|
||||
task_id, queue=queue, status_reason='k8s max pod limit (no free k8s service)')
|
||||
return
|
||||
pod_number += 1
|
||||
|
||||
@@ -271,29 +285,33 @@ class K8sIntegration(Worker):
|
||||
output, error = self._kubectl_apply(
|
||||
create_trains_conf=create_trains_conf,
|
||||
labels=labels, docker_image=docker_image, docker_args=docker_args,
|
||||
task_id=task_id, queue=queue)
|
||||
task_id=task_id, queue=queue, queue_name=safe_queue_name)
|
||||
else:
|
||||
output, error = self._kubectl_run(
|
||||
create_trains_conf=create_trains_conf,
|
||||
labels=labels, docker_image=docker_image,
|
||||
task_data=task_data,
|
||||
task_id=task_id, queue=queue)
|
||||
task_id=task_id, queue=queue, queue_name=safe_queue_name)
|
||||
|
||||
error = '' if not error else (error if isinstance(error, str) else error.decode('utf-8'))
|
||||
output = '' if not output else (output if isinstance(output, str) else output.decode('utf-8'))
|
||||
print('kubectl output:\n{}\n{}'.format(error, output))
|
||||
|
||||
if error:
|
||||
self.log.error("Running kubectl encountered an error: {}".format(error))
|
||||
elif self.ports_mode:
|
||||
user_props = {"k8s-pod-number": pod_number, "k8s-pod-label": labels[0]}
|
||||
if self._user_props_cb:
|
||||
# noinspection PyBroadException
|
||||
try:
|
||||
custom_props = self._user_props_cb(pod_number) if self.ports_mode else self._user_props_cb()
|
||||
user_props.update(custom_props)
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
user_props = {"k8s-queue": str(queue_name)}
|
||||
if self.ports_mode:
|
||||
user_props.update({"k8s-pod-number": pod_number, "k8s-pod-label": labels[0]})
|
||||
|
||||
if self._user_props_cb:
|
||||
# noinspection PyBroadException
|
||||
try:
|
||||
custom_props = self._user_props_cb(pod_number) if self.ports_mode else self._user_props_cb()
|
||||
user_props.update(custom_props)
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
if user_props:
|
||||
self._set_task_user_properties(
|
||||
task_id=task_id,
|
||||
**user_props
|
||||
@@ -312,12 +330,12 @@ class K8sIntegration(Worker):
|
||||
self.log.warning('skipping docker argument {} (only -e --env supported)'.format(cmd))
|
||||
return kube_args
|
||||
|
||||
def _kubectl_apply(self, create_trains_conf, docker_image, docker_args, labels, queue, task_id):
|
||||
def _kubectl_apply(self, create_trains_conf, docker_image, docker_args, labels, queue, task_id, queue_name):
|
||||
template = deepcopy(self.template_dict)
|
||||
template.setdefault('apiVersion', 'v1')
|
||||
template['kind'] = 'Pod'
|
||||
template.setdefault('metadata', {})
|
||||
name = 'trains-id-{task_id}'.format(task_id=task_id)
|
||||
name = 'clearml-{queue}-id-{task_id}'.format(queue=queue_name, task_id=task_id)
|
||||
template['metadata']['name'] = name
|
||||
template.setdefault('spec', {})
|
||||
template['spec'].setdefault('containers', [])
|
||||
@@ -354,7 +372,7 @@ class K8sIntegration(Worker):
|
||||
else:
|
||||
template['spec']['containers'].append(container)
|
||||
|
||||
fp, yaml_file = tempfile.mkstemp(prefix='trains_k8stmpl_', suffix='.yml')
|
||||
fp, yaml_file = tempfile.mkstemp(prefix='clearml_k8stmpl_', suffix='.yml')
|
||||
os.close(fp)
|
||||
with open(yaml_file, 'wt') as f:
|
||||
yaml.dump(template, f)
|
||||
@@ -380,11 +398,12 @@ class K8sIntegration(Worker):
|
||||
|
||||
return output, error
|
||||
|
||||
def _kubectl_run(self, create_trains_conf, docker_image, labels, queue, task_data, task_id):
|
||||
def _kubectl_run(self, create_trains_conf, docker_image, labels, queue, task_data, task_id, queue_name):
|
||||
if callable(self.kubectl_cmd):
|
||||
kubectl_cmd = self.kubectl_cmd(task_id, docker_image, queue, task_data)
|
||||
kubectl_cmd = self.kubectl_cmd(task_id, docker_image, queue, task_data, queue_name)
|
||||
else:
|
||||
kubectl_cmd = self.kubectl_cmd.format(
|
||||
queue_name=queue_name,
|
||||
task_id=task_id,
|
||||
docker_image=docker_image,
|
||||
queue_id=queue
|
||||
@@ -427,7 +446,7 @@ class K8sIntegration(Worker):
|
||||
:param queues: IDs of queues to pull tasks from
|
||||
:type queues: list of ``Text``
|
||||
:param worker_params: Worker command line arguments
|
||||
:type worker_params: ``trains_agent.helper.process.WorkerParams``
|
||||
:type worker_params: ``clearml_agent.helper.process.WorkerParams``
|
||||
"""
|
||||
events_service = self.get_service(Events)
|
||||
|
||||
@@ -1,4 +1,4 @@
|
||||
""" TRAINS-AGENT Stdout Helper Functions """
|
||||
""" CLEARML-AGENT Stdout Helper Functions """
|
||||
from __future__ import print_function, unicode_literals
|
||||
|
||||
import io
|
||||
@@ -28,8 +28,8 @@ from tqdm import tqdm
|
||||
|
||||
import six
|
||||
from six.moves import reduce
|
||||
from trains_agent.errors import CommandFailedError
|
||||
from trains_agent.helper.dicts import filter_keys
|
||||
from clearml_agent.errors import CommandFailedError
|
||||
from clearml_agent.helper.dicts import filter_keys
|
||||
|
||||
pretty_lines = False
|
||||
|
||||
@@ -380,11 +380,11 @@ AllDumper.add_multi_representer(object, lambda dumper, data: dumper.represent_st
|
||||
|
||||
|
||||
def error(message):
|
||||
print('\ntrains_agent: ERROR: {}\n'.format(message))
|
||||
print('\nclearml_agent: ERROR: {}\n'.format(message))
|
||||
|
||||
|
||||
def warning(message):
|
||||
print('trains_agent: Warning: {}'.format(message))
|
||||
print('clearml_agent: Warning: {}'.format(message))
|
||||
|
||||
|
||||
class TqdmStream(object):
|
||||
@@ -21,14 +21,14 @@ def start_check_update_daemon():
|
||||
|
||||
def _check_new_version_available():
|
||||
cur_version = __version__
|
||||
update_server_releases = requests.get('https://updates.trains.allegro.ai/updates',
|
||||
data=json.dumps({"versions": {"trains-agent": str(cur_version)}}),
|
||||
update_server_releases = requests.get('https://updates.clear.ml/updates',
|
||||
data=json.dumps({"versions": {"clearml-agent": str(cur_version)}}),
|
||||
timeout=3.0)
|
||||
if update_server_releases.ok:
|
||||
update_server_releases = update_server_releases.json()
|
||||
else:
|
||||
return None
|
||||
trains_answer = update_server_releases.get("trains-agent", {})
|
||||
trains_answer = update_server_releases.get("clearml-agent", {})
|
||||
latest_version = trains_answer.get("version")
|
||||
cur_version = cur_version
|
||||
latest_version = latest_version or ''
|
||||
@@ -48,7 +48,7 @@ def _check_update_daemon():
|
||||
if latest_version:
|
||||
if latest_version[1]:
|
||||
sep = os.linesep
|
||||
print('TRAINS-AGENT new package available: UPGRADE to v{} is recommended!\nRelease Notes:\n{}'.format(
|
||||
print('CLEARML-AGENT new package available: UPGRADE to v{} is recommended!\nRelease Notes:\n{}'.format(
|
||||
latest_version[0], sep.join(latest_version[2])))
|
||||
else:
|
||||
print('TRAINS-SERVER new version available: upgrade to v{} is recommended!'.format(
|
||||
@@ -9,7 +9,7 @@ from attr import attrs, attrib
|
||||
|
||||
import six
|
||||
from six import binary_type, text_type
|
||||
from trains_agent.helper.base import nonstrict_in_place_sort
|
||||
from clearml_agent.helper.base import nonstrict_in_place_sort
|
||||
|
||||
|
||||
def print_text(text, newline=True):
|
||||
@@ -5,8 +5,8 @@ from contextlib import contextmanager
|
||||
from typing import Text, Iterable, Union
|
||||
|
||||
import six
|
||||
from trains_agent.helper.base import mkstemp, safe_remove_file, join_lines, select_for_platform
|
||||
from trains_agent.helper.process import Executable, Argv, PathLike
|
||||
from clearml_agent.helper.base import mkstemp, safe_remove_file, join_lines, select_for_platform
|
||||
from clearml_agent.helper.process import Executable, Argv, PathLike
|
||||
|
||||
|
||||
@six.add_metaclass(abc.ABCMeta)
|
||||
@@ -67,7 +67,7 @@ class PackageManager(object):
|
||||
|
||||
def upgrade_pip(self):
|
||||
result = self._install(
|
||||
select_for_platform(windows='"pip{}"', linux='pip{}').format(self.get_pip_version()), "--upgrade")
|
||||
select_for_platform(windows='pip{}', linux='pip{}').format(self.get_pip_version()), "--upgrade")
|
||||
packages = self.run_with_env(('list',), output=True).splitlines()
|
||||
# p.split is ('pip', 'x.y.z')
|
||||
pip = [p.split() for p in packages if len(p.split()) == 2 and p.split()[0] == 'pip']
|
||||
@@ -15,14 +15,14 @@ import yaml
|
||||
from time import time
|
||||
from attr import attrs, attrib, Factory
|
||||
from pathlib2 import Path
|
||||
from trains_agent.external.requirements_parser import parse
|
||||
from trains_agent.external.requirements_parser.requirement import Requirement
|
||||
from clearml_agent.external.requirements_parser import parse
|
||||
from clearml_agent.external.requirements_parser.requirement import Requirement
|
||||
|
||||
from trains_agent.errors import CommandFailedError
|
||||
from trains_agent.helper.base import rm_tree, NonStrictAttrs, select_for_platform, is_windows_platform, ExecutionInfo
|
||||
from trains_agent.helper.process import Argv, Executable, DEVNULL, CommandSequence, PathLike
|
||||
from trains_agent.helper.package.requirements import SimpleVersion
|
||||
from trains_agent.session import Session
|
||||
from clearml_agent.errors import CommandFailedError
|
||||
from clearml_agent.helper.base import rm_tree, NonStrictAttrs, select_for_platform, is_windows_platform, ExecutionInfo
|
||||
from clearml_agent.helper.process import Argv, Executable, DEVNULL, CommandSequence, PathLike
|
||||
from clearml_agent.helper.package.requirements import SimpleVersion
|
||||
from clearml_agent.session import Session
|
||||
from .base import PackageManager
|
||||
from .pip_api.venv import VirtualenvPip
|
||||
from .requirements import RequirementsManager, MarkerRequirement
|
||||
@@ -132,7 +132,7 @@ class CondaAPI(PackageManager):
|
||||
if self.env_read_only:
|
||||
print('Conda environment in read-only mode, skipping pip upgrade.')
|
||||
return ''
|
||||
return self._install(select_for_platform(windows='"pip{}"', linux='pip{}').format(self.pip.get_pip_version()))
|
||||
return self._install(select_for_platform(windows='pip{}', linux='pip{}').format(self.pip.get_pip_version()))
|
||||
|
||||
def create(self):
|
||||
"""
|
||||
@@ -284,17 +284,11 @@ class CondaAPI(PackageManager):
|
||||
"""
|
||||
Try to install packages from conda. Install packages which are not available from conda with pip.
|
||||
"""
|
||||
try:
|
||||
self._install_from_file(path)
|
||||
return
|
||||
except PackageNotFoundError as e:
|
||||
pip_packages = [e.pkg]
|
||||
except PackagesNotFoundError as e:
|
||||
pip_packages = package_set(e.packages)
|
||||
with self.temp_file("conda_reqs", _package_diff(path, pip_packages)) as reqs:
|
||||
self.install_from_file(reqs)
|
||||
with self.temp_file("pip_reqs", pip_packages) as reqs:
|
||||
self.pip.install_from_file(reqs)
|
||||
requirements = {}
|
||||
# assume requirements.txt
|
||||
with open(path, 'rt') as f:
|
||||
requirements['pip'] = f.read()
|
||||
self.load_requirements(requirements)
|
||||
|
||||
def freeze(self, freeze_full_environment=False):
|
||||
requirements = self.pip.freeze()
|
||||
@@ -2,10 +2,10 @@ import sys
|
||||
from itertools import chain
|
||||
from typing import Text, Optional
|
||||
|
||||
from trains_agent.definitions import PIP_EXTRA_INDICES, PROGRAM_NAME
|
||||
from trains_agent.helper.package.base import PackageManager
|
||||
from trains_agent.helper.process import Argv, DEVNULL
|
||||
from trains_agent.session import Session
|
||||
from clearml_agent.definitions import PIP_EXTRA_INDICES, PROGRAM_NAME
|
||||
from clearml_agent.helper.package.base import PackageManager
|
||||
from clearml_agent.helper.process import Argv, DEVNULL
|
||||
from clearml_agent.session import Session
|
||||
|
||||
|
||||
class SystemPip(PackageManager):
|
||||
@@ -2,10 +2,10 @@ from typing import Any
|
||||
|
||||
from pathlib2 import Path
|
||||
|
||||
from trains_agent.helper.base import select_for_platform, rm_tree, ExecutionInfo
|
||||
from trains_agent.helper.package.base import PackageManager
|
||||
from trains_agent.helper.process import Argv, PathLike
|
||||
from trains_agent.session import Session
|
||||
from clearml_agent.helper.base import select_for_platform, rm_tree, ExecutionInfo
|
||||
from clearml_agent.helper.package.base import PackageManager
|
||||
from clearml_agent.helper.process import Argv, PathLike
|
||||
from clearml_agent.session import Session
|
||||
from ..pip_api.system import SystemPip
|
||||
from ..requirements import RequirementsManager
|
||||
|
||||
@@ -5,8 +5,8 @@ import attr
|
||||
import sys
|
||||
import os
|
||||
from pathlib2 import Path
|
||||
from trains_agent.helper.process import Argv, DEVNULL, check_if_command_exists
|
||||
from trains_agent.session import Session, POETRY
|
||||
from clearml_agent.helper.process import Argv, DEVNULL, check_if_command_exists
|
||||
from clearml_agent.session import Session, POETRY
|
||||
|
||||
|
||||
def prop_guard(prop, log_prop=None):
|
||||
@@ -14,12 +14,12 @@ from pathlib2 import Path
|
||||
from pyhocon import ConfigTree
|
||||
|
||||
import six
|
||||
from trains_agent.definitions import PIP_EXTRA_INDICES
|
||||
from trains_agent.helper.base import warning, is_conda, which, join_lines, is_windows_platform
|
||||
from trains_agent.helper.process import Argv, PathLike
|
||||
from trains_agent.session import Session, normalize_cuda_version
|
||||
from trains_agent.external.requirements_parser import parse
|
||||
from trains_agent.external.requirements_parser.requirement import Requirement
|
||||
from clearml_agent.definitions import PIP_EXTRA_INDICES
|
||||
from clearml_agent.helper.base import warning, is_conda, which, join_lines, is_windows_platform
|
||||
from clearml_agent.helper.process import Argv, PathLike
|
||||
from clearml_agent.session import Session, normalize_cuda_version
|
||||
from clearml_agent.external.requirements_parser import parse
|
||||
from clearml_agent.external.requirements_parser.requirement import Requirement
|
||||
|
||||
from .translator import RequirementsTranslator
|
||||
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user