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docs/getting_started/ds/best_practices.md
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@@ -22,19 +22,19 @@ During early stages of model development, while code is still being modified hea
the model and ensure that you choose a model that makes sense, and the training procedure works. Can be used to provide initial models for testing.
The abovementioned setups might be folded into each other and that's great! If you have a GPU machine for each researcher, that's awesome!
The goal of this phase is to get a code, dataset and environment setup, so we can start digging to find the best model!
The goal of this phase is to get a code, dataset, and environment setup, so you can start digging to find the best model!
- [ClearML SDK](../../clearml_sdk/clearml_sdk.md) should be integrated into your code (check out our [getting started](ds_first_steps.md)).
This helps visualizing the results and tracking progress.
- [ClearML Agent](../../clearml_agent.md) helps moving your work to other machines without the hassle of rebuilding the environment every time,
while also creating an easy queue interface that easily allows you to just drop your experiments to be executed one by one
while also creating an easy queue interface that easily lets you just drop your experiments to be executed one by one
(great for ensuring that the GPUs are churning during the weekend).
- [ClearML Session](../../apps/clearml_session.md) helps with developing on remote machines, just like you'd develop on you local laptop!
## Train Remotely
In this phase, we scale our training efforts, and try to come up with the best code / parameter / data combination that
yields the best performing model for our task!
In this phase, you scale your training efforts, and try to come up with the best code / parameter / data combination that
yields the best performing model for your task!
- The real training (usually) should **not** be executed on your development machine.
- Training sessions should be launched and monitored from a web UI.
@@ -55,8 +55,8 @@ that we need.
## Track EVERYTHING
We believe that you should track everything! From obscure parameters to weird metrics, it's impossible to know what will end up
improving our results later on!
Track everything--from obscure parameters to weird metrics, it's impossible to know what will end up
improving your results later on!
- Make sure experiments are reproducible! ClearML logs code, parameters, environment in a single, easily searchable place.
- Development is not linear. Configuration / Parameters should not be stored in your git, as