hotfixes in README.md
@@ -26,12 +26,12 @@ Our goal is to provide solutions for painful problems that ML engineers often ha
- :penguin: People using your infrastructure expect only one interface for all the things related to training models (besides terminal): monitoring, reservation calendar and scheduling distributed jobs
- :collision: Can't risk messing up sensitive configuration by installing software on each individual machine, prefering centralized solution which can be managed from one place
-2. You're a **standalone user** who has access to beefy GPUs scatterd across multiple machines.
+2. You're a **standalone user** who has access to beefy GPUs scattered across multiple machines.
- :part_alternation_mark: You want to be able to determine if batch size is too small or if there's a bottleneck when moving data from memory to GPU - charts with metrics such as `gpu_util`, `mem_util`, `mem_used` are great for this purpose
- :date: Visualizing names of training experiments using calendar helps you track how you're progressing on the project
- :snake: Launching distributed trainings is essential for you, no matter what the framework is
- :dizzy_face: Managing a list of training commands for all your distributed training experiments drives you nuts
-- :zzz: Remembering to manually launch the training before going sleep is no fun anymore.
+- :zzz: Remembering to manually launch the training before going sleep is no fun anymore
What TensorHive has to offer
-----------------------------
@@ -47,16 +47,16 @@ What TensorHive has to offer
:three: Gather all useful GPU metrics, from all configured hosts **in one dashboard**
-&nbsp;&nbsp;&nbsp;&nbsp; :arrow_right: no more manually logging in to each individual machine in order to check if GPU is currently in use or not
+&nbsp;&nbsp;&nbsp;&nbsp; :arrow_right: no more manual logging in to each individual machine in order to check if GPU is currently in use or not
-For more details, check out the [full list of features](#features)
+For more details, check out the [full list of features](#features).
Getting started
---------------
### Prerequisites
* All nodes must be accessible via SSH, without password, using SSH Key-Based Authentication ([How to set up SSH keys](https://www.shellhacks.com/ssh-login-without-password/) - explained in [Quickstart section](#basic-usage)
* Only NVIDIA GPUs are supported (relying on ```nvidia-smi``` command)
-* Currently TensorHive assumes that all users who want to register into the system must have identical UNIX usernames on all nodes configured by TensorHive administrator (not relevant to standalone developers)
+* Currently TensorHive assumes that all users who want to register into the system must have identical UNIX usernames on all nodes configured by TensorHive administrator (not relevant for standalone developers)
### Installation
@@ -93,7 +93,7 @@ tensorhive key
tensorhive test
```
-Now you should be ready to finally launch a TensorHive instance
+Now you should be ready to launch a TensorHive instance:
```
tensorhive
```
@@ -171,7 +171,7 @@ Features
#### Web
- [x] :chart_with_downwards_trend: Configurable charts view
- [x] Metrics and active processes
- - [ ] Detailed harware specification
+ - [ ] Detailed hardware specification
- [x] :calendar: Calendar view
- [x] Allow making reservations for selected GPUs
- [x] Edit reservations
